rafmacalaba/datause-encoder-data huggingface dataset:
estimated, which is guaranteed by Assumption 5, it follows that the estimator for _λd_ will be consistent. □ Finally, note that the sub-group inequality decompositions presented in Section 2.1 can readily be extended to accommodate the sub-division of the top tail into _D_ districts. (Note that the bottom segment can in principle stay as is, i.e. need not to be sub-divided into districts.) Let us denote the income share going to the top tail from district _d_ by _sd_ = _P_ 2 _,d_ ( _μ_ 2 _,d/μ_ ), where _μ_ 2 _,d_ = _E_ [ _Y |Y > τ, district d_ ]. Note that the population- and income shares corresponding to the bottom segment now solve 1 _−_<sup>�</sup> _d_<sup>_λd_and 1</sup><sup>_−_�</sup> _d_<sup>_sd_, respectively.Similarly, let us</sup> denote the Theil index or the mean-log-deviation for the top incomes from district _d_ by _T_ 2 _,d_ and _MLD_ 2 _,d_ , respectively. Using this notation, the decomposition of the Theil index and the mean-log-deviation into the 1 + _d_ sub-groups is seen to solve:

# **3 Data**

This paper uses two different types of data-sets: (1) Household Income, Expenditure and Consumption Survey (HIECS) data, and (2) listings of homes for sale derived from (large) real-estate databases. All data used in this study are for Egypt. The HIECS is from 2008/9. The house price data are slightly more recent, covering the period early 2013 to 2015, and come from two different real-estate firms. Details are given below.

## **3.1 Egyptian Household Income, Expenditure and Consumption Survey**

The Egypt HIECS 2008/9 is conducted by the Central Agency for Public Mobilization and Statistics (CAPMAS). We were given a 50% sample of the survey (approximately 24,000 observations).<sup>9</sup> Throughout the paper, our welfare aggregate is expenditure per capita which is consistent with standard practice in most developing countries. Household expenditures have been adjusted for spatial differences in prices by deflating nominal values by a spatial price index following

> 9Hlasny and Verme (2013) were able to access the 100% sample on site at CAPMAS.

13
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# **Alternative measure of market remoteness**

**Table 3: The impact of market remoteness on price volatility: Alternative measures of market remoteness**

|||(1)||(2)|
|---|---|---|---|---|
|Variables|Mean Equation|Variance Equation|Mean Equation|Variance Equation|
|Constant|4.1408***<br>(12.41)|0.0521***<br>(3.15)|3.8022***<br>(11.73)|0.0275<br>(1.78)|
|Ln_Pt-1_|0.9103***<br>(119.85)|-0.0003<br>(0.56)|0.9014***<br>(114.56)|0.0002<br>(0.51)|
|ARCH(1) term||0.1101***<br>(5.94)||0.0805***<br>(4.02)|
|Lean|0.0157***|0.0009***|0.0152***|0.0010***|
||(3.14)|(3.20)|(3.06)|(3.55)|
|Harvest|-0.0625***<br>(13.28)|0.0139***<br>(26.81)|-0.0638***<br>(13.51)|0.0135***<br>(22.93)|
|Trend|-0.0003|0.0000***|-0.0004|0.0000***|
||(1.15)|(5.80)|(1.47)|(6.24)|
|Exchange Rate|0.0746<br>(1.62)|-0.0035<br>(1.66)|0.0687<br>(1.48)|-0.0022<br>(1.12)|
|International Price|0.0044|-0.0017|0.0067|-0.0003|
||(0.20)|(1.78)|(0.30)|(0.34)|
|Kilometric distance|-0.0603***<br>(6.25)|0.0001***<br>(3.84)|||
|Road quality|||-0.1448***<br>(4.82)|0.0021***<br>(6.57)|
|Border|0.0527**|-0.0004***|0.0742***|-0.0006***|
||(2.28)|(3.29)|(2.79)|(4.01)|
|Surplus|-0.0835***<br>(2.97)|0.0016***<br>(5.62)|-0.0433<br>(1.28)|0.0012***<br>(4.46)|
|Markets Dummy|YES|NO|YES|NO|
|N|3163||3163||
|R2|0.8284||0.8286||

Note: (1) Market remoteness measure is the natural logarithm of the kilometric distance in minutes. (2) Market remoteness measure is the dummy road quality Values in parentheses are t-statistics *** and ** denote significance at 1% and 5% levels, respectively.

19
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42
# Benchmarking Global Poverty Reduction

## Martin Ravallion<sup>1</sup>

**Keywords** : Global poverty, relative poverty, social inclusion, growth, inequality, targets **JEL** : I32, O15

1 This paper was written in response to a question posed to the author by Jim Yong Kim about what targets might be set for future poverty reduction. Shaohua Chen assisted in the calculations. Helpful comments on an earlier draft were received from Francois Bourguignon, Shanta Devarajan, Francisco Ferreira, Sri Mulyani Indrawati, Jim Yong Kim, Peter Lanjouw, Ernesto May, Johan Mistiaen, Mahmoud Mohieldin, David Rosenblatt, Jaime Saavedra, Warrick Smith, Hans Timmer, Hasan Tuluy, Jos Verbeek, Dominique van de Walle and the many comments received from participants at the World Bank’s “Poverty Targets” working group, the Bank’s Poverty Reduction Board, and a seminar in the Bank’s research department. These are the views of the author, and need not reflect those of the World Bank or any affiliated organization or member country.
include Pharr (2000) who looks at aggregate data over time for one country (Japan); Della Porta (2000) who provides a verbal discussion of country-level averages of both corruption and confidence for just three countries; and Anderson and Tverdova (2003) who combine country-level data on corruption perceptions from the Transparency International Corruption Perceptions Index with household survey data on confidence from 16 mostly developed countries. The major drawback of such studies is that they cannot control for excluded country characteristics (or year effects in the case of Pharr (2000)) that very plausibly might confound the observed relationship between corruption and confidence in public institutions.

A second set of papers improves on these by relying on household-level variation in survey responses to questions about corruption and confidence to estimate the correlation between the two. These include Rose, Mishler, and Haerpfer (1998), Mishler and Rose (2001), Catterberg and Moreno (2005), and Chang and Chu (2006), who all document a negative partial correlation between perceptions of corruption and confidence in public institutions in small and regionally-focused samples of countries. These papers however do not recognize or attempt to address the identification problem to which we have referred in the introduction: it is unclear from the partial correlations documented by these authors whether respondents‟ perceptions of corruption drive their confidence in public institutions, or the converse. Also in this category is a somewhat different, but related paper by Hellman and Kaufmann (2004), who investigate how an alternative measure of corruption perceptions influences firms‟ confidence in, and use of, public institutions. They use data from the World Bank‟s Business Environment and Enterprise Performance Survey of 6500 firms in transition economies in 2002 to construct a measure of perceived „crony bias‟ as the difference between firms‟ perceptions of their own influence and the influence of other firms they view as having strong political connections. They show that firms who perceive a great deal of crony bias in policymaking have less confidence in the judiciary, are less likely to use courts, are more likely to pay bribes, and are more likely to cheat on their taxes.

Four more recent papers improve on the ones discussed so far by relying on respondent-level data on personal experiences with corruption (and not simply 5
respect to the probability that cases in a given country would be enforced. Notably, that there were 315 cases of corruption involving firms headquartered in the United States and only 56 in Germany (with enforcement occurring first in the respective countries) has no implications with respect to levels of corruption in the United States or in Germany. It is the _distribution_ of these cases (regardless of how many there are) _abroad_ which conveys information that could help determine the level of corruption in the countries where the bribery of foreign officials takes place.

# **The PACI**

The incidents of corruption involving firms in _i_ that implicate public officials in _j_ and first enforced in the headquarters’ country _i_ is indicated by _cases_ obs_ HQi_ , _j_ . The _PACIz_ compares the total number of those cases with the expected number of corrupt transactions that would be observed if their spatial distribution reflected bilateral trade shares between the headquarters countries and _z_ :

> The numerator, <mark></mark> _cases_  obs_  HQ i_ , _z_ , is the total number of observed corrupt exchanges

between officials from country _z_ and firms from all other headquarters countries, first enforced in those countries. The denominator is the total number of similar cases which could be observed if cases of corruption were distributed according to the ratio of exports of country _i_ to _z_ <mark>(</mark> _<mark>X</mark> iz_ <mark>)</mark> with respect to the total amount of country _i_ exports to the rest of the world: 7
Appendix Table 5b. Average individual statistics for children under three years old in 2008 by attrition

||Overall<br>average|Nonattrited<br>average|Attrited<br>average|Difference<br>(Nonattrited<br>- attrited)|Difference-in-<br>difference (DD)<br>migration &<br>attrition|
|---|---|---|---|---|---|
|Age in months|17.89|18.38|15.92|2.46 ***|0.69|
||(10.15)|(10.19)|(9.80)|(0.88)|(2.30)|
|Male = 1|0.51|0.49|0.61|-0.12 **|-0.03|
||(0.50)|(0.50)|(0.49)|(0.06)|(0.12)|
|HAZ|-2.17|-2.18|-2.13|-0.04|-0.07|
||(1.50)|(1.50)|(1.50)|(0.19)|(0.36)|
|Stunted (HAZ < -2) = 1|0.58|0.58|0.60|-0.03|0.14|
||(0.49)|(0.49)|(0.49)|(0.06)|(0.13)|
|WAZ|-1.08|-1.06|-1.17|0.11|0.52*|
||(1.11)|(1.12)|(1.07)|(0.13)|(0.30)|
|Underweight (WAZ <-2) = 1|0.19|0.20|0.17|0.04|-0.11|
||(0.40)|(0.40)|(0.37)|(0.04)|(0.09)|
|WHZ [N=514]|0.16|0.18|0.09|0.10|0.76 **|
||(1.24)|(1.25)|(1.19)|(0.13)|(0.29)|
|Wasted (WHZ < -2) [N=514]|0.04|0.05|0.02|0.03|-0.03|
||(0.20)|(0.22)|(0.14)|(0.02)|(0.03)|
|Father completed primary school = 1|0.12|0.12|0.14|-0.02|0.12|
||(0.33)|(0.33)|(0.34)|(0.05)|(0.09)|
|Mother completed primary school = 1|0.12|0.11|0.15|-0.03|0.02|
||(0.32)|(0.32)|(0.35)|(0.04)|(0.11)|
|Father is coresident = 1|0.77|0.78|0.73|0.05|-0.11|
||(0.42)|(0.42)|(0.45)|(0.05)|(0.13)|
|N|518|415|103|518|518|

Notes: Sample of children under three years old in 2008. Migrant household is defined as a household with at least one migrant to the U.S. in three of the four years 2005-08. Nonattrited indicates child has valid measurement in 2012. Standard deviations in parentheses under average and standard errors estimated allowing for clustering at the sample cluster level in parentheses under difference and difference-in-difference (DD) . The DD shows averages for (nonattrited migrant - attrited migrant) - (nonattrited non-migrant - attrited non-migrant). *** indicates significantly different at p<0.01, ** at p<0.05, and * at p<0.10.
# **3.3 Public remote sensing data**

The analysis uses publicly available global remote sensing data from numerous sources and summarizes predictors derived from them at the administrative level (i.e. TA level) for Malawi. For most of the variables, we take advantage of the geographically comprehensive raster data structure, which uses pixels to form a grid that covers all land areas. From the raster structure we extract geo-referenced information at the administrative level. Using Geographic Information Systems (GIS) and Google Earth Engine, we overlay the administrative boundaries with other geo-referenced gridded data, (e.g. temperature, precipitation, elevation, etc.). When an administrative area consists of multiple pixels (which is typically the case), the pixel observations are collapsed (i.e. aggregated) to obtain summary statistics (e.g. mean and sum) at the administrative area level.<sup>7</sup> This process of extraction provides a nearly exhaustive correspondence. For a select few cases, we make two minor adjustments:

- Administrative areas near boundaries with water or small islands do not always overlay with the raster data. In this case, we first extract data by center point if the results from the polygon extract are missing. Second, we make a small adjustment by filling in the missing cells from the original raster with the average value of data from contiguous neighbors.

- When the polygon representing an administrative area is much smaller than the raster pixel size, we disaggregate the raster data. If the center point of the polygon is located in an area of “no data” (e.g. a lagoon or a lake), then we move the center point to the nearest land pixel. We also calculate the area at the pixel size to ensure consistency between the sum of pixel values and the corresponding land area.<sup>8</sup>

For the remaining data, we use vector data to calculate distances. Table 3 lists the variables considered in this paper that have been derived from publicly available remote sensing datasets. These variables are assembled into various themes related to initial conditions, agriculture, climate, environment, socio-economic conditions, population and remoteness. Specific details on the source and construction of the variables can be found in Appendix 2.

> 7 We use two geometries (center points and polygons) to represent the administrative area. We extract raster data by polygon using the extract function in R. In Google Earth Engine, we use the Reducer function over a region at the administrative level.

> 8An area calculation with the vector data provides more accurate measurements, however it does not always yield consistent area with the pixel calculations, which is necessary to ensure variables with the share are between 0 and 1.

16
of the existing teachers, which is the other robust result in the panel regressions presented in Table 5 (again, we use the most conservative estimate). Thus, the estimates in column 6 account for the fact that the marginal rate of absence from hiring an extra teacher is higher than the average absence rate, and that increased spending on hiring teachers is correlated with an increase in inefficiency.

The difference in the relative cost effectiveness of the two policy options is large enough, that the policy recommendation of hiring more supervisors rather than teachers (on the current margin) would be unchanged even if the supervisors were to work less efficiently than assumed in these calculations. For instance, if supervisors were absent at the same rate as teachers (say 25 percent), allocating marginal funds to hire an additional supervisor would still be nearly ten times more cost effective at reducing ESTR than using those funds to hire an additional teacher.

# **6 Policy Implications**

The main caveat to using our results to recommend a universal policy of hiring more supervisors to scale up the frequency of school inspections is that our estimates are based on correlations and may not be convincing enough to warrant a universal scale up. Nevertheless, it is worth noting that both our key results - the correlation between increased monitoring and reduced teacher absence, and the correlation between lower STR and increased teacher absence - are consistent with experimental evidence from smaller-scale, which increases our confidence in their validity. Further, our estimates are based on an expansion of existing system of inspections, and use nationwide panel data (which mitigates omitted variables concerns) representing close to a billion people, and complement results from smaller-scale randomized experiments warranting them greater external validity for several reasons.

First, while our results support results from smaller randomized experiments, there is evidence that experimentally-estimated positive results of interventions that are implemented by NGOs may not be replicated when the programs are implemented by governments (Banerjee, Duflo and Glennerster, 2008). Second, there is also evidence of site-selection bias where implementing partners are more likely to be willing to rigorously evaluate programs in locations where they are more likely to be successful (Allcott, 2015). Finally, even in the absence of such a bias, most experiments are conducted in very few sites, and may yield imprecise treatment effects (for inference over a larger population) in a setting where unobserved site-specific covariates may interact with the treatment (Pritchett and Sandefur, 2013).<sup>29</sup>

> 29The largest education experiments to date that we know of have been conducted over five districts in one state of India (Muralidharan and Sundararaman, 2010, 2011, 2013). While these experiments feature random assignment in representative samples of schools (in a state with over 80 million people), they still

20
20 statistical measure, and kerosene had the lowest pass-through, signifying the reluctance of many governments to raise the price of kerosene for household use.

Table 3: Summary of pass-through statistics for January 2009–January 2012, all countries

|**Parameter**|**Gasoline**|**Diesel**|**Kerosene**|**LPG**|
|---|---|---|---|---|
|Number of countries|73|73|45|52|
|Average pass-through (%)|83|71|56|78|
|Median pass-through (%)|95|84|61|75|
|||**Percentage**|**of countries**||
|Pass-through greater than 50%|71|70|56|58|
|Pass-through greater than 75%|60|55|38|50|
|Pass-through greater than 100%|42|34|20|38|
|Pass-throughgreater than 150%|10|1|0|13|

_Sources_ : Tables A2.1 and A2.3, and author’s calculations.

Statistics by income, oil trade status, and region for each fuel can be found in tables 4–7. As expected, net oil importers were much more likely to pass through price increases on the world market. For gasoline, diesel, and kerosene, the percentage of countries with pass-through coefficients higher than 75 percent and the median pass-through increased with rising income. This income trend is somewhat surprising for gasoline, which would generally be a fuel of the rich in low-income countries. For LPG, the degree of pass-through was highest for low-income countries, although this needs to be interpreted with caution because there were only seven countries in that income group. Because LPG use occurs most frequently among better-off urban households in low-income countries, this pattern of pass-through as a function of income would be progressive. These relationships between the country’s income and pass-through hold when the findings are confined to net importers. By region, Latin America and the Caribbean had the highest pass-through for every fuel by various measures—median, mean, and the percentage of countries with coefficients greater than 75 percent. East Asia and the Pacific ranked second for gasoline, diesel, and kerosene, and South Asia for LPG. Europe and Central Asia are excluded from consideration because there were not enough countries in the sample.
**Table 6. Employment patterns prior to the state split**

# **<u>(1998, town and village level)</u>**

|||Log (<br>|employme<br>|nt)<br>||||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|0.2911|0.1864|0.1887|0.1219|0.0645|0.1032|0.1135|
||(1.94)|(1.44)|(1.58)|(1.09)|(0.64)|(1.11)|(1.31)|
|_No. of observations_|1962|2526|3033|3542|4111|4628|5128|
|||Log|(total firm|s)||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||40 kms|50 kms|60 kms|70 kms|80 kms|90 kms|100 kms|
|_Target_|0.1748|0.1284|0.1459|0.1030|0.0848|0.1138|0.1102|
||(1.55)|(1.31)|(1.60)|(1.19)|(1.10)|(1.62)|(1.68)|
|_No. of observations_|1962|2526|3033|3542|4111|4628|5128|

_Note:_ The analysis is based on towns and villages that are matched to the 2013 Economic Census. All regressions include a _constant_ , _Distance_ and _Distance X Target_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

**Table 7. Employment trends prior to the state split** **<u>(1990-1998, town and village level)</u>**

|||ΔL<br>|og(employm<br>|ent)<br>||||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|-0.3040|-0.3187|-0.3089*|-0.2411|-0.2860*|-0.2473*|-0.2449*|
||(-1.56)|(-1.87)|(-1.99)|(-1.75)|(-2.31)|(-2.14)|(-2.23)|
|_No. of observations_|1057|1333|1591|1870|2157|2398|2642|
|||Δ<br>|Log (total fir<br>|ms)<br>||||
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|-0.2430|-0.1656|-0.1184|-0.0933|-0.1232|-0.0888|-0.0933|
||(-1.76)|(-1.37)|(-1.06)|(-0.93)|(-1.40)|(-1.09)|(-1.20)|
|_No. of observations_|1057|1333|1591|1870|2157|2398|2642|

_Note:_ The analysis is based on towns and villages that are matched to the 2013 Economic Census. All regressions include a _constant_ , _Distance_ and _Distance X Target_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

25