rafmacalaba/datause-encoder-data huggingface dataset:
households in the central part of the income distribution, and one lower, which focuses on households in the upper part of the income distribution.

Many studies indicate that global vulnerability and economic and social inequalities increased in recent decades (Pleninger, Haan, and Sturm 2022), despite the rapid decline in absolute poverty (Kharas 2017; Kochar and Oates 2015; Burrows 2015).

Globally, the middle class has been affected by the simultaneous trends of globalization, automation, and job polarization (Pleninger, Haan, and Sturm 2022; Derndorfer and Kranzinger 2021; Salido and Carabaña 2020). A shrinking middle class implies that individuals may experience more difficulties reaching or maintaining their middle-class status and are more vulnerable to falling into poverty (OECD 2019).

Historically, Europe has been a middle class society with low levels of inequality (Ranci et al. 2021). However, in recent years, between-group inequality has been growing (Callan, Doorley, and Savage 2018). Estimates on the size of the middle class using different absolute and relative definitions show little movement in size, despite growing perceptions of inequality (Bussolo et al. 2018). There has not been, however, an assessment of the evolution of the middle class in the region using a vulnerability approach. This paper is the first to apply such approach used, for instance, in Latin America (López-Calva and OrtizJuarez 2014), to the case of Europe.

# **III. Methodology**

The vulnerability approach identifies members of the middle class using three variables: a poverty line, a low probability of falling into poverty, and a permanent income threshold that guarantees that households earning at least such an income face such a low probability risk of becoming poor.

The poverty line is set at $24.35<sup>1</sup> per day.<sup>2</sup> A household in the European Union is considered middle class if its permanent income is equal to or above the level required to maintain the specified low risk of falling

> 1 All dollar figures in this paper are in 2017 purchasing power parity (PPP).

> 2 The vulnerability-based approach to the middle class relies on an absolute poverty concept and thus on an absolute poverty line. Eurostat has established a relative poverty measure (“at-risk of poverty”) which relies on a relative poverty line equal to 60 percent of the median household equivalized income in a given year. Since this poverty line is a function of the income distribution, it is not fixed over time or across countries. An at-risk of poverty rate of 20 percent in one country does not mean the same thing in another (and a change in at-risk of poverty might reflect a change in the income distribution rather than a change in the share of the population living under a certain level of

6
infrastructure have left DRC one of the poorest countries in the world, with the average Congolese resident living on less than $US0.75 per day. And yet, geography and natural endowments give DRC the potential to become one of the richest countries in the region. It is estimated that DRC’s unexploited mineral wealth stands at US$24 trillion (UNEP 2011). Additionally, with over 22.5 million hectares of uncultivated, unprotected, non-forested fertile land, it is suggested that DRC has the potential to become the breadbasket of Africa, and feed over 1 billion people (Deininger and Byerlee 2011).

Harnessing the growth potential of these endowments is not without challenges. With violence having declined since the end of the second Congolese civil war in 2003, DRC now seems poised to make investments to spur future economic growth. However, the state of DRC’s road infrastructure is severely deficient even by the standards of other low-income countries (see table 1). It is striking that only four provincial capitals out of ten can be reached by road from the national capital, Kinshasa. Improvement in road infrastructure will undoubtedly need to include significant road improvement and construction projects. However, with conflict still erupting in parts of the country, this raises the question of whether improvements in transport infrastructure will bring benefits to local economies. There is also the possibility that such interventions may have perverse effects and provide violent militias easier access to vulnerable and remote communities, or tempt subsistence farmers to join the militias. This paper attempts to shed light on these questions, by studying the interaction between conflict within DRC and transport infrastructure.

**Table 1.**

|Indicator|Units|Low-Income<br>Country Average|DRC|
|---|---|---|---|
|Paved Road Density|km/1000 km<sup>2</sup>of land|16|1|
|Unpaved Road Density|km/1000 km<sup>2</sup>of land|68|14|
|Paved Road Traffic|Average daily traffic|1,028|257|
|Unpaved Road Traffic|Average daily traffic|55|20|
|Perceived Transport<br>Quality|% firms identifying as major<br>business constraint|23|30|

Source: The Democratic Republic of Congo’s Infrastructure: A Continental Perspective, March 2010 In order to motivate and contextualize our empirical analysis, we first present a simple theoretical model to analyze how transport costs and conflict are interlinked and how they directly and jointly affect 3
**Table 2: Average wage rates (in Dinars per hour) and population shares by gender and level of education.**

||_All Emp_|_loyees_|_Private, Non_|_agriculture_|_Non-p _|_rivate_|
|---|---|---|---|---|---|---|
||Population<br>shares|Average<br>Wages<br>Rate|Population<br>shares|Average<br>Wages<br>Rate|Population<br>shares|Average<br>Wages<br>Rate|
|**Gender**|||||||
|Male|59%|72.2|55%|73.9|61%|70.5|
|Females|41%|56.0|45%|47.5|39%|66.2|
|**Education**|||||||
|High-skilled|23%|86.4|12%|81.8|34%|88.1|
|Low-skilled|77%|59.3|88%|59.4|66%|58.9|
|**Gender and Education**|||||||
|Low-skilled females|30%|46.8|39%|43.8|22%|52.5|
|Low-skilled males|47%|67.3|49%|71.5|44%|62.0|
|High-skilled females|11%|80.1|6%|70.2|17%|83.7|
|High-skilled males|11%|92.6|6%|93.6|17%|92.5|
|**Total**|100%|65.5|100%|62.1|100%|68.9|

Source: Author’s estimates using 2006 Macedonia LFS data. * High-skill workers are defined as those with at least complete higher education (university and above).

**Table 3: Unconditional gender wage gap and occupational segregation [low-skilled workers only]**

||_Share of e_|_mployment_|_Average _|_wage rate_|||
|---|---|---|---|---|---|---|
||Male|Female|Male|Female|| Mi/M –<br>Fi/F ||**Gender**<br>**Gap**|
||(1)|(2)|(3)|(4)|(5)|(3) / (4)|
|Agriculture/mining/fishing|6.01|2.33|65.3|40.5|0.04|41.2%|
|Manufacturing|26.66|44.41|55.5|40.2|0.18|32.0%|
|Elec/Gas/Water|5.71|1.55|78.2|72.3|0.04|7.6%|
|Construction|14.87|1.76|101.3|44.6|0.13|58.5%|
|Wholesale/retail|12.24|14.57|55.9|48.0|0.02|15.2%|
|Hotels/Restaurants|4.61|4.25|95.8|51.5|0.00|55.7%|
|Transport/communication|8.78|2.32|62.6|67.1|0.06|-7.1%|
|Fin & Real State Svs.|2.48|4.22|65.8|71.2|0.02|-7.9%|
|Public Admin.|9.65|5.16|68.0|55.9|0.04|18.7%|
|Educ./health/Social Work|4.64|16.13|52.9|50.3|0.11|5.0%|
|Other services|4.35|3.31|60.9|59.2|0.01|2.9%|
|**Index of segregation**|||||**0.33**||

Source: Author’s estimates using 2006 Macedonia LFS data. The formula for computing the Index of Dissimilarity by industry is: _D_ = 5.0 × ( _M i M_ ) − ( _Fi F_ ) where M (F) is the male (female) population of employees and Mi (Fi) is the male (female) population employed in industry _i_ .

12
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**TABLE 8: Effects of the improvements on the aggregates and the lock-in effect**

||**Base**|**Expansi**<br>**cap**<br>(_Z_ increa|**on of road**<br>**acity**<br>sed 20%)|**Improv**<br>**transit tr**<br>(<br>2<br>_G_ decrea|**ement of**<br>**avel time**<br>sed 20%)|**Road e**<br>**and**<br>**impro**|**xpansion**<br>**transit**<br>**vement**|
|---|---|---|---|---|---|---|---|
|Car equivalent traffic<br>6<br>,10|2.4326|2.6729|_+9.98%_|2.359|_-3.0%_|2.5943|_+6.65%_|
|Car person trips,<br>6<br>,10|3.603|4.0632|_+12.77%_|3.3725|_-6.40%_|3.8339|_+6.41%_|
|Bus person trips<br>6<br>,10|3.7373|3.5112|_-6.05%_|4.3185|_+15.55%_|4.0348|_+7.96%_|
|Car travel time (hours)<br>6<br>,10|3.0906|3.092|_+0.05%_|2.7724|_-10.30%_|2.8058|_-9.22%_|
|Car vehicle kilometers<br>7<br>,10|7.4484|8.3998|_+12.77%_|6.9719|_-6.40%_|7.9258|_+6.41%_|
|Fuel used by cars (liters)<br>6<br>,10|7.2393|7.6184|_+5.24%_|6.6064|_-8.74%_|7.0401|_-2.75%_|
|CO2emitted by cars (mil.tons/yr)|4.6059|4.7434|_+2.98%_|4.1732|_-9.40%_|4.3487|_-5.59%_|
|Speed of car travel (km/hr)|24.1|27.2|_+12.77%_|25.15|_+4.35%_|28.25|_+17.21%_|

In order to properly capture the lock-in effect, four simulations needed to be done and the results are shown in the corresponding columns of Table 8. The first is a base simulation representing the calibrated state corresponding to 2002. The second column shows what happens if highway capacity is expanded by 20%. Speed, aggregate vehicle-kilometers and trips by car all increase by 12.7%, bus trips decrease by 6%, and car equivalent traffic increases by 9.8%. The aggregate travel time of those commuting by car increases only slightly by 0.04%, reflecting the effect of the road capacity expansion. Fuel consumption increases by 5.2% and CO2 emissions by almost 3%. As expected, improving transit (column 3 of Table 8) has oppositely signed effects, except on car speed which increases as people switch from cars to the less congesting buses in larger numbers than walkers and cyclists switch to buses. In fact, the 20% improvement in bus travel time increases car speed by 1/3 as much as increasing road capacity by 20% did. Note that fuel consumption and emissions decrease by around nine percent. Now in the last step the road capacity improvement and the transit travel time improvement are introduced simultaneously. In this case note that emissions improve relative to base but by about 5.6% instead of the 9.4% improvement that occurred when transit had no competition from road improvements. In the case of fuel the reduction in the presence of highway improvements is 24
neighboring roads are much larger compared to the sample with all farmers. These findings have important implications - in order to maximize the efficiency of motorized freight transportation, it is important to simultaneously upgrade feeder roads and enhance competition on the trucking industry.

**Table 3: The determinants of tobacco transport costs to markets: tobacco farmers using lorry transportation**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dependent variable:|l|n(a farmer’s toba|cco transport costs|)|
|Estimation||OLS,with IHS2|samplingweights||
|Sample|tob|acco farmers usin|glorrytransportat|ion|
|ln(transport volume)|0.798***|0.786***|0.802***|0.800***|
||(0.026)|(0.027)|(0.025)|(0.026)|
|ln(transport distance)|0.170***|0.161***|0.109***|0.121***|
||(0.023)|(0.025)|(0.032)|(0.035)|
|ln(distance to asphalt roads)||0.052***||0.026*|
|||(0.013)||(0.014)|
|ln(road traffic volume,ADT)|||-0.087***|-0.071***|
||||(0.019)|(0.021)|
|Constant|Yes|yes|yes|Yes|
|Observations|850|802|727|697|
|R-squared|0.661|0.660|0.684|0.677|

Note: 1. Robust standard errors in parentheses 2. * significant at 10%; ** significant at 5%; *** significant at 1% 17. In summary, a farmer’s transport costs of taking tobacco to markets are determined by both supply and demand side factors. Poor feeder road quality, which links farms to main roads and low traffic volume (limited competition and less trucking demand) on neighboring main roads raise transport costs. These findings are consistent with Africa wide studies that highlight infrastructure constraints (Pedersen 2001), low levels of competition between service providers (Rizet and Hine 1993), and weak infrastructure (Limao and Venables 2001) as contributors to high transport costs.

# **Asking truckers about transport prices**

18. To complement the analysis of transport costs faced by farmers, a survey of transport providers was carried out to understand how a combination of vehicle operating costs, indirect costs and profit margins contributed to the setting of transport prices. This survey builds on the trucking survey instrument and methodology developed in the World Bank Africa region’s transport unit, and described in Teravaninthorn and Raballand (2008). Figure 3, from that report identifies the various components of transport prices.

- 8 -
**Table 4:  India - Contribution by inputs**

||TFP|Capital|Labor|
|---|---|---|---|
|Textile|1.01|-0.06|0.00|
|Leather|0.98|-0.02|0.05|
|Garman|0.93|0.04|0.08|
|FOOD|0.98|0.01|-0.02|
|Machine|0.93|0.07|0.04|
|Electronics (w/o outlier)|1.02|-0.05|-0.01|
|Chemistry|0.99|-0.02|0.03|

_Note: the above results excludes outliers with absolute value for growth in capital or output larger than 200_ When we combine the contribution of inputs to the growth rate in India for the period 2004-05, it can be seen that the contribution of TFP is 0.75, that of capital is 0.25 and labor is about 0.05. This shows that as we aggregate the data series, the relative higher weight of TFP drops. This is also confirmed by the analysis for Brazil, results of which are presented below.

**Table 5:  Brazil estimation by industries**

||Textiles|Leather|Garments|Food|Metals<br>and<br>machinery|Electronics|Chemicals<br>and<br>pharmaceutics|Wood<br>and<br>furniture|Auto and<br>auto<br>components|
|---|---|---|---|---|---|---|---|---|---|
|lcapital_n|0.13|0.32***|0.37***|0.43***|0.07|0.55**|0.35|0.23***|0.25***|
||(0.72)|(4.40)|(4.50)|(2.81)|(0.38)|(2.54)|(1.54)|(3.49)|(2.95)|
|lworker_edu|0.88***|0.96***|0.88***|0.74***|1.16***|0.49|0.83|1.20***|0.94***|
||(3.18)|(7.72)|(7.20)|(4.02)|(7.41)|(1.29)|(1.56)|(9.70)|(6.52)|
|Constant|2.66***|0.54|0.31|0.87*|1.74*|1.17|1.78|-0.11|1.24***|
||(4.67)|(1.35)|(0.80)|(1.68)|(1.76)|(1.10)|(1.30)|(-0.30)|(3.38)|
|Observations|36|74|187|58|93|28|43|133|67|
|R-squared|0.69|0.83|0.65|0.77|0.59|0.54|0.51|0.79|0.87|

_Note 1: Robust t‐statistics in parentheses, *** p<0.01, ** p<0.05, * p<0.1; note 2: the above results excludes outliers with absolute value for growth in capital or output larger than 200, note 3:_ excluding outliers with absolute value for growth in capital or output larger than 200)

**Table 6:  Brazil - Contribution by inputs**

||TFP|Capital|Labor|
|---|---|---|---|
|Textile|0.87|0.00|0.12|
|Leather|0.84|0.12|0.11|
|Garment|0.87|0.03|0.13|
|FOOD|1.12|-0.08|-0.14|
|Material / machine|0.87|-0.02|0.10|
|Elec|0.88|0.17|-0.02|
|Chemistry|1.04|0.04|-0.09|
|Wood|0.81|0.11|0.13|
|Auto|0.98|-0.01|0.01|

_Note: the above results excludes outliers with absolute value for growth in capital or output larger than 200_ 15
11

# Figure 4: Food-related government policies

## (A) Interventions in agricultural markets

- (B) Policy interventions during the 2007-08 food price spike

<!-- Start of picture text --> Percent Reference price Percent<br>1.05 Nominal rate of protection (RHS) 20 Percent of respondents<br>60<br>1.00<br>16<br>40<br>0.95<br>12<br>0.90 20<br>8<br>0.85 0<br>4<br>0.80<br>0.75 0<br>(C) Insulation and correction coefficients (D) Increase in world prices, 2010-11<br>Coefficient Insulation Correction Percent With active policy<br>0.0 50 Without active policy<br>-0.2 40<br>-0.4<br>30<br>-0.6<br>-0.8 20<br>-1.0<br>10<br>0<br>Rice Wheat Maize<br>2011 2012<br>Sugar<br>None<br>taxes Price stocks<br>subsidies<br>2005 2006 2007 2008 2009 2010 2013 2014 2015<br>Reduce foodgrain controls/consumer Increase foodgrain Export restrictions<br>Pork Rice Beef Poultry Wheat Maize Soybean<br><!-- End of picture text --> Sources: Ag-Incentives Database, Ivanic and Martin (2014b), World Bank. A. Nominal Rate of Protection (NRP) is computed as the price difference between the farm gate price received by producers and an undistorted reference price at the farm gate level. The reference price at the farm gate level is defined as the net price of the product when it leaves the farm, after marketing costs have been subtracted. The undistorted farm gate price is defined as the price prevailing in competitive world markets.

B. Percent of respondents based on a survey of 80 EMDEs.

C. Estimates based on the Error Correction Model described in Annex 6.1. The coefficient of price insulation ranges from 0 for countries that do not insulate against the rise in world prices, to -1 for countries that adopt policies that fully insulate domestic markets. The error correction term represents the cost of being out of equilibrium or the speed with which polices achieve the target level of protection or at which policy makers move back toward this equilibrium after being forced away from it by a shock to world prices. Based on data for 82 countries, of which 26 advanced economies, 44 EMDEs, and 12 LICs for the period 1955-2011.

D. Real terms. Estimates derived based on the methodology described in the Annex.
Policy Research Working Paper 8776

# **Abstract**

Country-level census data are typically collected once every 10 years. However, conflict, migration, urbanization, and natural disasters can cause rapid shifts in local population patterns. This study uses Sri Lankan data to demonstrate the feasibility of a bottom-up method that combines household survey data with contemporaneous satellite imagery to track frequent changes in local population density. A Poisson regression model based on indicators derived from satellite data, selected using the least absolute shrinkage and selection operator, accurately predicts village-level population density. The model is estimated in villages sampled in the 2012/13 Household Income and Expenditure Survey to obtain out-of-sample density predictions in the nonsurveyed villages. The predictions approximate the 2012 census density well and are more accurate than other bottom-up studies based on lower-resolution satellite data. The predictions are also more accurate than most publicly available population products, which rely on areal interpolation of census data to redistribute population at the local level. The accuracies are similar when estimated using a random forest model, and when density estimates are expressed in terms of population counts. The collective evidence suggests that combining surveys with satellite data is a cost-effective method to track local population changes at more frequent intervals.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at vidhyasrajan@iimb.ac.in.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Figure A.2: Average log unit value by district <!-- Start of picture text --> AlcoholTobacco Dairy Fruit Legumes<br>.913256<br>-1.21277<br>Maize Meat OilsFats OtherFood<br>.913256<br>-1.21277<br>Rice Sugar Vegetables Wheat<br>.913256<br>-1.21277<br>difference from the log mean 1 32 1 32 1 32 1 32<br><!-- End of picture text -->

# Federal District Graphs by good

Source: Author’s calculation using INEGI households surveys 1984-2000.

33
products, oil and fats, vegetables and dairy products.<sup>32</sup> Unit values for fruits, sugar and wheat are constant, while maize-based products appear to be the only product that experienced an increase in the unit value.<sup>33</sup> In order to be suitable for elasticity estimates, the data must show a certain degree of variability across spatial locations and time. Even if unit values are not the same as prices because of the quality issue<sup>34</sup> and measurement errors<sup>35</sup> , from inspection of figure 1 and 2 it is sufficiently clear that the unit values, and likely prices, for most of the products show a substantial variation across cluster.

Figure A.2 shows the logarithm of unit values of the ten product groups across the 32 federal districts. Sugar, meat, oil and fats seem to have the most stable unit value across districts. Meanwhile, there is a higher variance in the unit value for alcohol and tobacco, dairy products, fruit, legumes, maize, vegetables and wheat. Overall, there seems to be enough spatial and time variation to support the methodology undertaken in this paper.

> 32 The unit values are adjusted by the inflation rate of food products and are reported in year 2000 terms. 33 The unit values of alcoholic beverages and tobacco products are calculated only starting from the year 1994 because of a different classification in the first three household surveys.

> 34 Two unit values may result different even if the underlining price is the same because the goods purchased are of different quality.

> 35 Data on household surveys is usually subject to measurement error. This may be due to the construction of the questionnaire, processing of the data, misreporting and survey design.

31
6 percent of the cereal production) and remain an important part of their income. Millet and sorghum, two other important staple grains, are cultivated more widely in drier parts of SSA. Their humidity content is usually lower when harvested, rendering them more resistant to post-harvest pest attacks than maize (World Bank, 2011b).

It is found that between 7 (Malawi) and 22 (Uganda) percent of maize farmers report to incur on-farm PHL for maize, losing between 21 to 27 percent of their harvest. This adds up to 1.4 to 5.9 percent of the national maize harvest being lost on-farm which is substantially lower than the FAO (2011) post-harvest handling and storage loss estimate for cereals in SSA of 8 percent as well as the APHLIS estimates of 12-20 percent (all PHL before marketing but without processing). PHL increases with storage duration, humidity and temperature and declines with better market access, higher seasonal price differences, and likely also with improved storage practices. With at most 5 percent of the households applying both, adoption of improved storage practices remains however rather limited.

The paper proceeds as follows. Section two presents the PHL estimates and the underlying data in more detail, including the stated reasons for the reported losses and the prevalence of adopted storage and crop protection technologies. The behavioral and market drivers of PHLs are subsequently examined in more detail in a multivariate setting in section three using the 2008/9 survey round from Tanzania. Section four concludes.

# **2 On-farm PHL for maize is limited, but concentrated**

The nationally representative household surveys from Malawi (2010-11), Tanzania (two years: 2008-09 and 2010-11), and Uganda (2009-10) collected under the LSMS-ISA initiative form the information base of the study. In addition to standard survey modules on household characteristics, assets, income and consumption, they contain detailed self-reported crop-level information on PHLs, their stated causes, and the storage and crop protection modalities used together with post-harvest management features, such as the amount still stored and the timing and amount of past sales. Geo-