rafmacalaba/datause-encoder-data huggingface dataset:
suppliers to become more efficient. For instance, Mexican-owned detergent producers have reported introducing incremental improvements to their products in order to avoid drastic price cuts demanded by Wal-Mart (Javorcik et al. 2006).

Second, entry of global chains possessing cutting-edge retail technologies and familiar with best international practices may help lower costs faced by suppliers. Rather than sending their products to a large number of small retailers, suppliers may deliver larger shipments to several retail outlets. Thanks to computerized inventory systems used by global retail chains, suppliers may be better informed about changes in demand and may be better able to tailor products to the expectations of consumers. For instance, Wal-Mart provides its suppliers with full and free access to real-time data on how their products are selling. Suppliers can plan production runs earlier and offer better prices (Economist 2001). Tesco tracks every purchase through its Club card and can use this information to help its private-label suppliers to test and adapt innovations (The Boston Consulting Group 2007). Saving on employee time and usage of capital (e.g. truck fleet) when arranging distribution and planning production, suppliers may produce more output with the same amount of labor and achieve higher total factor productivity. Finally, global retail chains could stimulate economies of scale among suppliers by offering producers a larger market (both in the host country as well as abroad).

In sum, by increasing competitive pressures on suppliers, cutting distribution costs and offering easier access to information and a larger market global retail chains may stimulate productivity growth in the supplying industries.

# **4. Data**

This study examines the link between the expansion of global retail chains and developments in the supplying industries in the context of Romania. Focusing on Romania has three advantages. The first advantage is the availability of high quality and comprehensive firmlevel data. We have time-varying information on 513,554 companies operating in Romania during the period 1996-2005. The data set contains information on firms of all sizes, including those with one employee. As small or medium-sized enterprises (SMEs) in the supplying industries may be affected to a different degree than large companies, being able to include them in the analysis is an advantage. The second advantage of using Romanian data is the timing of the entry of global retailer chains. They started entering Romania only in the mid-1990s which 6
_table 2. Mean Values of Student-Level Variable: by Scho/ltype_ (standard deviations in parentheses)

|_Variable_|_All_<br>_Scl.ools_|_Former_<br>_GroupA _|_F ,mer_<br>_Group B_|_High Fee_<br>_Paying_|_Low Fee_<br>_Paying_|_Distric;_<br>_Council_|
|---|---|---|---|---|---|---|
|Grade 7 English score|_5,?_|8.2|5.7|8.2|5.7|4.6|
||(2.2)|(1.2)|(1.9)|(1.7)|(2.0)|(1.9)|
|Grade 7 mathematics score|4.9|6.8|4.7|6.8|5.2|4.1|
||(2.2)|(1.9)|(2.1)|(2 9)|(2.1)|(2.0)|
|Age in years|13.7|12.9|13.5|12.7|13.9|14.1|
||(1.5)|(0.8)|(I.1)|(1.2)|(1.4)|(1.7)|
|Years taken to reach Gr. 7|';. 2|7.1|7.1|7.2|7.2|7.4|
||(0.6)|(0.4'|_(0.5)_|(0.5)|_(0.5)_|**(0.9)**|
|Nunmber of children in family|6.6|5.8|5.7|_5.4_|7.1|7.7|
||(3.9)|(2.1)|(3.7)|(1.8)|(4.0)|(4.4)|
|father's education|||||||
|No education|44%|7%|42%|14%|439%|66%|
|Primary|16%|_5%_|19%o|_5%_|18%|18%|
|Junior Certificate|24%|36%|30%|19%|29%|13%|
|O-Level|10%|25%|9%|28%|8%|2%|
|A-Level or higher|6%|26%|1%|35%|2%|1%|
|Mother's education|||||||
|No education|52%|12%|_50%_|11%|54%|_75%_|
|Primary|20%|16%|24%|9%|23%|18%|
|Junior Certificate|19%|_45%_|23%|23%|19%|6%|
|O-Level|6%|14%|4%|31%|3%|1%|
|A-Level or higher|4%|14%|-|26%|1%|-|

_Noe.:_ For categorical variables percentages in each categorv are shown 3-4 years of teacher training.' The highest proportion of untrained teachers was found in the district council schools-25%. Further teacher characteristics underline the discrepancies in teachers' This statistic seems high and could retlect either inflated reporting on the part of the teachers surveyed or a misunderstanding of the various categories of qualification. About 50% of primary school teachers were untrained in 1990. However, it is possible that within schools, the allocation of trained teachers is more in favor of the Grade 7 classes, thus accounting for this discrepancy. Such an hypothesis would be borne out in part if school level repornog of percentages of trained teachers were closer to the expected figures. As one will see below, the figures at the school lean in this direction, but higher proportions of 'standard trained' teachers are still reported tian what would be expected.

6
# **<u>Table 5: Poisson regressions for the number of projects in a destination country</u>**

||||**All projects**|||
|---|---|---|---|---|---|
|Potentially cultivable area non-forest|0.4946***<br>[0.121]|||||
|Potentially cultivable area forest|-0.0205<br>[0.070]|||||
|Max. possible output value non-forest area||0.5257***<br>[0.108]|0.5296***<br>[0.110]|0.5122***<br>[0.139]|0.5086***<br>[0.140]|
|Max. possible output value forest area||-0.0159|-0.0405|-0.0523|-0.0658|
|||[0.060]|[0.060]|[0.064]|[0.064]|
|Yield gap|0.6033|0.9061*|1.1224**|-0.2444|-0.0245|
||[0.416]|[0.474]|[0.524]|[0.710]|[0.696]|
|Land governance indicator||||-0.1735**<br>[0.078]|-0.1779**<br>[0.081]|
|Weak investor protection|||-0.0017<br>[0.003]||-0.0022<br>[0.003]|
|No. of observations|137|143|135|107|105|
|Pseudo R<sup>2</sup>|0.325|0.297|0.293|0.290|0.292|
|||**Projects wi**|**th some prod**|**uction only**||
|Potentially cultivable area non-forest|0.6876***<br>[0.156]|||||
|Potentially cultivable area forest|-0.0083<br>[0.094]|||||
|Max. possible output value non-forest area||0.6840***|0.6894***|0.6199***|0.6148***|
|||[0.139]|[0.145]|[0.165]|[0.166]|
|Max. possible output value forest area||-0.0435|-0.0684|-0.0606|-0.0734|
|||[0.077]|[0.079]|[0.079]|[0.080]|
|Yield gap|0.1057|0.5362|0.7641|-0.3172|-0.0872|
||[0.517]|[0.617]|[0.753]|[1.003]|[1.017]|
|Land governance indicator||||-0.1422|-0.1456|
|||||[0.108]|[0.109]|
|Weak investor protection|||-0.0019||<br>-0.0023|
||||[0.003]||[0.003]|
|No. of observations<br>|137|143|135|107|105|
|Pseudo R<sup>2</sup>|0.346|0.271|0.266|0.230|0.229|

**_Notes:_** Variable in logs. Robust standard errors in brackets. *** denotes significance at the 1% level, ** at the 5% level, and * at the 10% level. Constant included but not reported.

24
|_E_(_Y |T_ = 1_, X_)_−E_(_Y |T_ = 0_, X_)<br>(3)<br>(4)<br>(5)<br>OLS<br>OLS<br>IV<br>0.200*<br>0.256***<br>0.279**<br>(0.110)<br>(0.085)<br>(0.109)<br>0.163*<br>0.201***<br>0.256***<br>(0.088)<br>(0.072)<br>(0.094)|0.142*<br>0.163**<br>0.205**<br>(0.085)<br>(0.074)<br>(0.096)<br>0.146*<br>0.143*<br>0.181*<br>(0.088)<br>(0.078)<br>(0.100)<br>0.055<br>0.043<br>0.069<br>(0.100)<br>(0.088)<br>(0.111)<br>No<br>Yes<br>Yes|ts” refers to any bed nets, irrespective of their treatment status,<br>built using self-reported information about how many months<br>port sample means restricted to control and treatment group,<br> between treatment and control groups using OLS regression<br>rt to have received the spraying campaign in the last 5 months<br>ment group indicator (model 3). Standard errors clustered at<br>cation and marriage status of the respondent, household size,<br>, sub-zone dummies and village characteristics (size and share|
|---|---|---|
|(1)<br>(2)<br>Control<br>Treatment<br>Current stock<br>1.575<br>1.774<br>[1.207]<br>[1.279]<br>1 month before interview<br>1.958<br>2.122<br>[1.033]<br>[1.101]|3 months before interview<br>1.829<br>1.971<br>[1.095]<br>[1.131]<br>6 months before interview<br>1.708<br>1.854<br>[1.127]<br>[1.144]<br>12 months before interview<br>1.297<br>1.352<br>[1.158]<br>[1.171]<br>Controls|Note: *** p_<_0.01, ** p_<_0.05, * p_<_0.1. One observation per household. “Ne<br>“ITNs” includes only LLINs and properly treated ITNs. Stock over time is<br>before the interview the household acquired the net. Columns (1) and (2) re<br>standard deviations in brackets. Columns (3) and (4) report the difference <br>(model2). Column (5) estimates the difference between households who repo<br>and those who didn’t by instrumenting program participation with the treat<br>village level are reported in parentheses. Controls include gender, age, edu<br>tribe and religion, information about access to water, dwelling characteristics<br>of women living in the village).|

26
to CPI bias. This assumption may not hold if there are additional, unobserved forces that shift the Engel curve - such as changes in preferences, or variations in the way consumption data are collected. Analysis in Gibson, Le and Kim (2016), for example, suggests that the method does not perform well in a spatial context. A similar concern is that even though the method controls for changes in household characteristics, biases can arise from assumptions about the nature of relationship between these covariates and the food share. For example, Logan (2009) argues that the effect of household size on demand varies over time and that this is not captured by the standard Engel curve estimation because of the way demographic effects are modeled. Using household survey data on the United States from 1888 and 1935, Logan (2009) finds that when changing household size effects are taken into account, estimates of CPI bias are reduced by 25 percent.<sup>5</sup> A related critique is that Engel curve estimates conflate various types of CPI bias and are hence difficult to interpret. As noted earlier, Engel curve estimations are used primarily to tackle biases arising from commodity and outlet substitution. However, as argued by Beatty and Crossley (2012), the method may conflate the above form of CPI bias with inflation inequality across the distribution (plutocratic bias). This may be problematic, in particular, if Engel curve estimations are used to make statements and recommendations referring to the quality of the CPI and its methodology. Plutocratic weights, for example, are preferred for some purposes of the CPI, such as the deflation of economic aggregates (Pollak 1998) and are hence (at least partly) an explicit component in the design of the index.<sup>6</sup>

# **III. Empirical methodology**

Our starting point is the Leser-Working form of the food Engel curve, which is defined as a linear function of the log of real total expenditure and a relative price term, as follows:

> 5 However, it is unclear whether the household size correction decomposes or modifies CPI bias estimates.

> 6 It is not entirely clear how important plutocratic bias is in practice. Case studies show that distributional differences in inflation can be significant in times of rapidly changing prices, e.g. after a drought or other supply side shock (Günther and Grimm 2007). Studies for the US, on the other hand, often find little systematic inequality in inflation across the income distribution (Kokoski 2000; Kaplan and Schulhofer-Wohl 2016).

8
|T||1|SummarySta|isticsand|alance||||||
|---|---|---|---|---|---|---|---|---|---|---|
||||||Househol|dlevelrando|mization|Village|levelrand|omization|
||||Insample||(lottery|participant|sonly)|(non-trea|ted househ|olds only)|
|||Non-|Lottery|p-valueof|||p-valueof|Low|High|p-valueof|
||InCensus <br>N=1578|participants <br>N=183|participants<br>N=528<br>|__ test:<br>=(2)=(3)|Control<br>~N=262|Treated<br>N=266|test:<br>(5)=(6)|access<br>N=256|access<br>N=189|test:<br>(8)=(9)|
|HouseholdHeadCharacteristics|(1)|(2)|(3)|<br>(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Is male|0.935|0.934|0.949|0.461|0.954|0.929|0.211|0.918|0.979|0.006|
|Age|45.913|49.443|46.384|0.014|45.771|46.936|0.342|46.914|47.196|0.845|
|Ismarried|0.156|0.186|0.117|0.020|0.897|0.929|0.198|0.875|0.910|0.244|
|Ispolygamous|0.237|0.240|0.295|0.154|0.332|0.342|0.808|0.289|0.280|0.842|
|Numberofspouses|1.172|1.126|1.305|0.018|1.305|1.421|0.111|1.223|1.265|0.554|
|Hasnoformal schooling|||||0.706|0.714|0.836|0.715|0.730|0.722|
|Hascompletedprimary|||||0.103|0.102|0.953|0.086|0.085|0.962|
|<br>Hascompletedsecondary|||||0.011|0.011|0.985|0.008|0.011|0.760|
|<br>OtherHouseholdCharacteristics|||||||||||
|Householdsize|8.238|7.486|9.373|0.000|9.210|10.038|0.060|8.375|8.926|0.207|
|<br>Number ofcrops|||||4.378|4.338|0.795|4.102|4.302|0.227|
|Total value of crops (inUSD)|||||1619.970|1739.327|0.491|1483.621|1543.083|0.760|
|Landowned|||||7.408|8.133|0.423|7.034|6.028|0.166|
|Trust, confidenceandneighboringvillage relat|ions||||||||||
|Interactswithneighbouring,villagemonthly||0.514|0.563|0.253|0.557|0.568|0.810|0.527|0.556|0.556|
|<br>Confident storing inneighboringvillage||0.596|0.652|0.176|0.634|0.669|0.392|0.559|0.698|0.003|
|Neighboringvillagememberwouldreturnlost|wallet|0.268|0.292|0.538|0.324|0.259|0.101|0.324|0.270|0.217|
|<br>Confidence in institutions index||0.086|-0.009|0.715|-0.029|0.011|0.881|-0.195|0.308|0.089|
|Kaxgutazespecific|||||||||||
|<br>Familiarwithwarrantage.|0.807|0.760|0.869|0.000|0.782|0.786|0.927|0.684|0.804|0.004|
|<br>Wouldstore:Sorghum|0.282|0.230|0.326|0.014|0.317|0.335|0.663|0.266|0.302|0.405|
|<br>Would store: Maize|0.565|0.514|0.657|0.001|0.660|0.654|0.882|0.598|0.603|0.907|
|Wouldstore:Millet|0.209|0.208|0.222|0.694|0.218|0.226|0.825|0.172|0.270|0.013|
|<br>#ofbagstostoreiftakingpartinwarrantage,|«6.745|5.175|8.063|0.001|7.172|8.940|0.069|5.898|6.963|0.221|

Notes: In sample refers to all households who were interviewed in the baseline survey. Participants and non-participants refer to those households who did and those who did not take part in the warrantage lottery respectively. Each row presents the proportion or means of the respective variable for the designated group. Column (4), (7), and (10) present p-values to test whether the means between the identified groups are the same.
<!-- Start of picture text --> i % | P @«<br>0-5 yg/m3 «<br>5 - 10 pg/m3 FY<br>I) 10 - 15 pg/m3<br>MMB 15 - 25 ug/m3<br>MMM 25 - 35 g/m3<br>MB 35 - 145 pq/m3<br><!-- End of picture text -->
18

**Figure 11  Percentage of people living in households that cited LPG as the primary cooking fuel who are estimated to have abandoned polluting fuels**

<!-- Start of picture text --> Urban<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : The analysis is confined to those areas—nationally or urban areas—where more than one-fifth of the population lived in households citing LPG as the primary cooking fuel. Only national results were available for Mauritius. In Burkina Faso, Kenya, and Nigeria, the one-fifth threshold was met only in urban areas and hence national results are not shown.

Analysis of LPG expenditure shares shows that the 5 percent threshold suggested in the multi-tier framework as an upper limit for affordability was not breached in any of the sampled areas. In Figure 12, the blue bars represent LPG expenditure shares of all households citing LPG as the primary cooking fuel, whether or not they had abandoned polluting fuels. The green bars take the segment of the households in the blue bars who had abandoned polluting fuels. The differences are small. This finding seems surprising at first. First, abandonment of polluting fuels would be expected to increase the quantities of LPG purchased, everything else being equal. Second, measurement errors caused by asking households how much they had spent on LPG in the previous month would be expected to be smaller for those using only LPG: many more of those who had not abandoned other fuels would be expected to purchase LPG less frequently than once a month, resulting in under-estimation of expenditures on LPG, although overestimation by those who happened to have purchased LPG in the previous month might have offset such under-estimation. In countries where a large fraction of those using LPG as the primary cooking fuel had not abandoned polluting fuels despite spending little on LPG—Cameroon, Sudan, and urban Burkina Faso—it is not clear that financial considerations had prevented these households from switching entirely to LPG (and electricity) for cooking.
24 If, for example π=0.1, then Π *1.0 is simply the ordered set of types, ranked by mean advantage, up until the type that brings the population share of the set over 10%. Table 7 lists the circumstances that define the types in Π *1.0 for our sample of 30-49 evermarried women in Turkey, when imputed consumption is chosen as the relevant advantage. Such a detailed profile might permit identifying those groups most deserving of policy support, from the perspective of a Rawlsian social planner who adopted an equality of opportunities perspective to define social groups. See Roemer (2006), and Bourguignon, Ferreira and Walton (2007).

While Table 7 describes each type in the opportunity-deprivation profile individually, Table 8 better summarizes the composition of the bottom and top tenths of the (consumption) opportunity profile in Turkey. This table reveals that 99% of those women in the most advantaged group were born in urban areas, while 88% of the bottom tenth was born in rural areas. 95% of the bottom tenth of the opportunity profile was born in Eastern provinces, and 97% had mothers with no formal education whatever. A similar proportion was born in households where Turkish was not the primary language spoken, and over 70% had six or more siblings. The contrast between the two columns in Table 8 is stark: when Turkish women are ranked by the mean imputed consumption of their types, and we look at the bottom and top tenths of the ensuing distribution, they come from strikingly different backgrounds, geographically, educationally and ethnically.

# **7. Conclusion**

Rising interest in inequality of opportunity among both normative and positive economists has led to various recent attempts to measure it empirically. However, because the measurement of inequality of opportunity generally requires reasonably detailed data on both a measure of advantage (such as income or consumption) and on a set of pre-determined background circumstances (such as parental education, wealth or occupation), these attempts have run afoul of data limitations in a number of countries. The most common problem has been the absence of information on the parents of today’s adults in the same surveys that document the incomes or consumption expenditures of those adults.
There are two indicators of water infrastructure utilized in our analysis. The first is the number of incidents of water shortages per day in a typical month. Firms are asked how many incidents of insufficient water supply their establishment experienced in a typical month, over the last fiscal year. The mean of the number of water shortages per day in a typical month in the sample is 0.02, which translates to about 11 water shortages per year. The second measure is the average duration of water outages in hours. This measure takes a value of zero for firms that do not experience any water shortages. The mean value for the sample (including those firms that did not experience any outages) is 0.86 hours. For the sample of firms that experienced water outages, the average duration of a water shortage is approximately 10 hours.

Our analysis also includes corresponding measures of electricity infrastructure, as the performance of water and electricity infrastructure may be correlated, and we do not wish to confound one with the other. As with the water indicators, measures of electricity infrastructure performance include the number of power outages per day in a typical month and the average duration of these outages. The mean of the number of power outages per day in a typical month is 0.26, which translates to about 119 power outages per year. The mean value for the duration of power outages for the whole sample is 2.19 hours. For the sample of firms that experienced power outages, the duration of a power outages is approximately 4 hours. These statistics show that fewer firms experience water shortages than electrical outages, but for those that do, water shortages last far longer than electrical outages. Summary statistics are presented in table 1, while descriptions and sources of variables are presented in table A1.

The ES data are combined with weather data obtained from Matsuura and Willmott (2012a,b). The weather data set contains monthly observations of precipitation and average temperature at the 0.5-degree grid cell level. The data are transformed into average monthly temperature, and total precipitation (mm), per year, for each grid cell. Rather than analyzing the impact of rainfall averages, we look at the impact of rainfall “shocks”, i.e., unanticipated deviations from average values. To account for the non-normal distribution of rainfall, the raw rainfall data are converted to a Standardized Precipitation Index, following the methodology outlined by Neves (2015), before calculating the “shocks”; further details are provided in the next section. As illustrated in table 1, droughts (SPI<-1.5) are a more common occurrence than situations of abundant rainfall (SPI>1.5) 6
**Figure 3.5: Access to fixed and cellular telephones**

<!-- Start of picture text --> 80<br>70<br>60<br>Both fixed and cellular<br>50 lines<br>Cellular line only<br>40<br>30 Fixed line only<br>20<br>10<br>0<br>1 2 3 4 5<br>Consumption quintile<br>Percentage of households<br><!-- End of picture text --> _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística - Guatemala

# **3.4 The remaining deficit**

Notwithstanding this progress, a significant coverage gap remains (Table 3.9). Well over half a million households are still without electricity and piped water. Some 200,000 are without any form of sanitation, while about 1.3 million rely on latrines as opposed to conventional sewerage. The households that remain unserved are predominantly rural and predominantly poor.

**Table 3.9: Coverage gap for modern utilities**

<u>(Number of unserved households)</u>

||Electricity|Piped water<sup>♣</sup>|Basic sanitation<sup>♦</sup>|Improved sanitation|Total no. of households|
|---|---|---|---|---|---|
|National|585,933|686,893|288,807|1,353,895|2,191,451|
|By area||||||
|Urban|45,189|113,235|24,156|224,291|951,654|
|Rural|540,744|573,658|264,651|1,129,604|1,239,797|
|By quintile||||||
|1|266,931|220,182|116,340|411,318|438,437|
|2|155,116|163,797|84,249|349,173|427,908|
|3|98,428|164,199|52,064|304,708|446,068|
|4|44,513|104,894|25,003|203,850|442,583|
|5|20,945|33,821|10,161|84,846|436,455|

**Notes:**<sup>♣</sup> piped water in dwelling or yard;<sup>♦</sup> includes toilets and latrines. _Source:_ World Bank calculations using the ENCOVI 2000, Instituto Nacional de Estadística – Guatemala

From Table 3.7 it can be inferred that the typical annual rate of service expansion at present rates was around 115,000 new connections for electricity, water and sanitation. Given current levels of population growth of around 2.6% per annum, with this rate of expansion it will take more than eight years to reach universal coverage for all services except for basic sanitation (Table 3.10). Only a doubling of current rates of expansion, or a stabilization of population, would permit universal coverage to be reached in the medium term; that is between 3 to 12 years depending on the service.

24