rafmacalaba/datause-encoder-data huggingface dataset:
**Table S1.2: average value of household food consumption per capita by household structure - all food commodities**

|Household type|PSF survey<br>Mean<br>(N)|ESPS survey<br>Mean<br>(N)|Difference<br>PSF-ESPS<br>(Std. err.)|
|---|---|---|---|
|Single person households|570,530<br>(98)|502,432<br>(540)|68,098<br>(38,759)|
|Other one cell households|289,300<br>(147)|262,873<br>(901)|26,427<br>(16,621)|
|More than one cell 'nuclear' hh.|217,181<br>(549)|193,024<br>(3,971)|24,156<br>(17,379)|
|Other households|181,343|150,434|30,909|
||(968)|(8,147)|(5,321)|
|All households|223,162|181,548|41,614|
||(1,762)|(13,559)|(6,746)|

Source: « Pauvreté et Structure Familiale » (PSF) and « Enquête de Suivi de la Pauvreté au Sénégal » (ESPS) surveys, authors' calculations.

Note: Summary statistics of food consumption per capita, by household types. In the first two columns, the sample size is reported between parentheses. In the third column, the standard error of the difference is reported.

**Table S1.3: average value of household total consumption per capita by household structure - all commodities**

|Household type|PSF survey<br>Mean<br>(N)|ESPS survey<br>Mean<br>(N)|Difference<br>PSF-ESPS<br>(Std. err.)|
|---|---|---|---|
|Single person households|1,086,946|1,000,344|86,602|
||(98)|(540)|(84,672)|
|Other one cell households|531,182|535,947|-4,764|
||(147)|(901)|(52,169)|
|More than one cell 'nuclear' hh.|392,651|351,172|41,479|
||(549)|(3,971)|(22,022)|
|Other households|367,697|301,915|65,783|
||(968)|(8,147)|(14,561)|
|All households|429,115|371,231|57,884|
||(1,762)|(13,559)|(12,859)|
|All individuals|328,135|284,917|43,218|
||(13,988)|(123,486)|(3,519)|

Source: « Pauvreté et Structure Familiale » (PSF) and « Enquête de Suivi de la Pauvreté au Sénégal » (ESPS) surveys, authors' calculations. Note: Summary statistics of total consumption per capita, by household types. In the first two columns, the sample size is reported between parentheses. In the third column, the standard error of the difference is reported.

# **3. Poverty and inequality**

We now compare the values of poverty and inequality indices obtained with the two surveys.

Two poverty lines are considered. The lowest, « nutrition », line corresponds to the level of expenditure necessary to purchase enough food to attain a minimum caloric intake of 2400 kcal per adult equivalent and per day. Since in PSF we do not have a reliable set of price data, we use the poverty lines computed by Ndoye et al. (2009), that we update using the 13
42 TABLE 12

|||AVERAGE C<br>AND PRICE-|APITAL-OUTPUT<br>COST<br>MARGINS<br>IN IMPORT|RATIO, CAP<br>OF PRIVATE<br>ABLE<br>INDUST|ITAL-LABOUR<br> <br>AND PUBLIC<br>RIES|RATIO<br>FIRMS||
|---|---|---|---|---|---|---|---|
|OBS|YEAR|AKORPR|APCMPR|AKORPU|APCMPU|AKLP|R<br>AKLPU|
|I<br>|1976<br>|0.309713<br>|0.166433<br>|1 24271<br>|0.114464<br>|1559.<br>|14<br>2080.71<br> <br>|
|2<br>3|1977<br>1978|0.322264<br>0343430|0.171696<br>|0.72523<br>|0 094775<br>|o93.<br>|09<br>2149.86<br> <br>|
|<br>4|<br>1979|.<br>|0.243047<br>|1.18151<br>|-0.048879<br>|859<br>|98<br>2148 58<br> <br>|
|<br>5|<br>1980|0.433601<br>21|0.159921<br>|1.2622t<br>|-0.014781<br>|1872<br>|60<br>2229.14<br> <br>|
|<br>6|<br>1981|0.5486<br>0432535|0.201852<br>|1.28301<br>|-0.079552<br>|1962<br>|52<br>2203 47<br> <br>|
|<br>7|<br>1982|.<br>0406763|0.179306<br>|1.01133<br>|-0.050858<br>|2124.<br>|3<br>2232.10<br> <br>|
|<br>8|<br>1983|.<br>|0.235824<br>|0.97805<br>|0.135739<br>|2137.<br>|7<br>2266.96<br> <br>|
|<br>9|<br>1984|0.388929<br>0369391|0.164868<br>0181966|0.94840<br>|0.111884<br>|2302.<br>|7<br>2102.01<br> <br>|
|<br>10|<br>1985|.<br>0405910|.<br>|0.83336<br>|0.093228<br>|2388.<br>|1<br>2135 33<br> <br>|
|||.|0.172139|0.82107|0.059929|2695.|5<br>2143.64|

TABLE 13

|AV<br>OF|ERAGE CAP<br>PRIVATE<br>|ITAL-OUTPUT<br>AND PUBLIC<br>|RATIO AND<br>FIRMS IN NON-|PRICE COST<br>TRADABLE<br>|MARGINE<br>INDUSTRIES|
|---|---|---|---|---|---|
|085|YEAR|AKORPR|APCMPR|AKORPU|APCMPU|
|1<br>|1976<br>|0.420770<br>|0.192049<br>|1.34678<br>|0.300481|
|2<br>|1977<br>|0.463053<br>|0.220253<br>|2.16261<br>|0.141636<br>|
|3<br>|1978<br>|0.478445<br>|0.217046<br>|3.33284<br>|0.126357<br>|
|4<br>|1979<br>|0.575112<br>|0.197578<br>|1.48524<br>|0 038053<br>|
|5<br>|1980<br>|0.673856<br>|0.190084<br>|2.02724<br>|-0.021055<br>|
|6<br>|1981<br>|0.538128<br>|0.234080<br>|2.01738<br>|0.202471<br>|
|7<br>|1982<br>|0.481381<br>|0.246952<br>|2.20898<br>|0.237747<br>|
|8<br>|1983<br>|0.550217<br>|0.234613<br>|1.94025<br>|0.243315<br>|
|9<br>|1984<br>|0.585416<br>|0.225677<br>|1.79802<br>|0.181423<br>|
|10|1985|0.536734|0.203223|0.96472|0.212998|

AKORPR-AVERAGE CAPITAL-OUTPUT RATIO IN THE PRIVATE SECTOR AKORPU-AVERAGE CAPITAL-OUTPUT RATIO IN THE PUBLIC SECTOR APCMPR.AVERAGE PRICE-COST MARGIN IN THE PRIVATE SECTOR APCMPU-AVERAGE PRICE-COST MARGIN IN THE PUBLIC SECTOR AKLPR-AVERAGE CAPITAL-LABOR RATIO IN THE PRIVATE SECTOR AKLPU-AVERAGE CAPITAL-LABOR RATIO IN THE PUBLIC SECTOR
37 ) political violence and fraud; constituencies of parties; legality of parties; and the endurance of parties over time. Using this aggregate level of data organization, many qualities of the political system can be captured by a very few variables, and the net effect of formal and informal (harder to observe and codify) influences can be summarized. The disadvantage of this information is that those seeking specific details on the electoral system will find this information inextricably entangled with the other measures comprising the variable.

By comparison, the DPI uses variables that reflect only one dimension. For example, many variables are dummies, focusing on questions such as “is the closed list system used?”, “is the chief executive in the military?”, or “was there electoral fraud?”. Examples of non-dummy variables include the seats, ideological orientation, and government/opposition affiliation of political parties; details on the level of federalism; the elapsed time in office of the chief executive; and the timing of elections. This database thus presents data at a disaggregated level. With some exceptions, the DPI generally avoids the use of variables that wrap several different measures into one number. The creation of such variables is left to the user. The exceptions to this are the Indices of Electoral Competitiveness and the CHECKS variables. The electoral competitiveness variables take into account rules governing whether parties can run and-actual outcomes — whether parties do run and win seats. The CHECKS variables draw on several variables within the database to construct an index of checks and balances in the political system. While our variables therefore do not necessarily reflect the informal constraints on electoral competition or executive authority, they allow researchers to identify the extent of formal constitutional control on political decision makers. In addition to methdological differences in the two databases (subjective and aggregated, but evaluating both formal and informal conditions in a country, compared to

|
Policy Research Working Paper 7627

# **Abstract**

Unanticipated spikes in food prices can increase malnutrition among the poor, with lasting consequences; however, livelihood strategies that include producing food for home consumption are expected to offer a measure of protection. Using anthropometric and consumption data from Indonesia collected before and after the 2007/08 food price crisis, this paper finds evidence of both effects. Based on standardized height and weight measures, the results indicate that soaring food prices had a significant and negative impact on child growth among non-farming households. A corresponding effect was undetectable for food-producing households. The results remain robust when income effects from increased commercial sales and possible attritions through migration and fostering are considered. Further, local food price changes were uncorrelated with the share of non-farming village households and the initial average child nutrition status in the village, suggesting that the observed outcomes are directly attributable to market events and livelihood strategies. Interestingly, gender differences were not detected. The findings imply that the food price crises can have negative impacts on children, potentially leading to lifelong income inequality among those affected at a vulnerable stage of life.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at fyamauchi@worldbank.org.

_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
- Rudin-Rush, L., J. D. Michler, A. Josephson, and J. R. Bloem (2022). Food insecurity during the first year of the COVID-19 pandemic in four African countries. _Food Policy 111_ , 102306.

- Sadish, D., A. Adhvarya, and A. Nyshadham (2021). (Mis)information and anxiety: Evidence from a randomized COVID-19 information campaign. _Journal of Development Economics 152_ , 102699.

- Smith, L. C. and T. R. Frankenberger (2018, February). Does Resilience Capacity Reduce the Negative Impact of Shocks on Household Food Security? Evidence from the 2014 Floods in Northern Bangladesh. _World Development 102_ , 358–376.

- Smith, M., M. Rabbitt, and A. Coleman-Jensen (2017). Who are the world’s food insecure? new evidence from the food and agriculture organization’s food insecurity experience scale. _World Development 93_ , 402–12.

- Stevens, A. W. and J. Teal (2024). Diversification and resilience of firms in the agrifood supply chain. _American Journal of Agricultural Economics, 106_ (2), 739–78.

- Stoop, N., S. Desbureaux, A. Kaota, E. Lunanga, and M. Verpoorten (2021). COVID-19 vs. ebola: Impact on households and small businesses in North Kivu, Democratic Republic of Congo. _World Development 140_ , 105352.

- Welderufael, M. (2014). Analysis of households vulnerability and food insecurity in Amhara Regional State of Ethiopia: Using value at risk analysis. _Ethiopian Journal of Economics 23_ (2), 37–77.

- World Bank (WB) (2022). Ethiopia - COVID-19 High-Frequency Phone Survey of Households 2020. Public Use Dataset. Ref: ETH_2020_HFPS_v08_M. Dataset downloaded from `https: //doi.org/10.48529/m9pb-0d05` .

- Wossen, T., T. Berger, M. G. Haile, and C. Troost (2018, June). Impacts of climate variability and food price volatility on household income and food security of farm households in East and West Africa. _Agricultural Systems Perspectives on Global Food Security 163_ , 7–15.

23
Existing general equilibrium studies, such as Al-Riffai et al. (2010) also find similar impacts on agricultural output. For example, expansion of biofuels to meet EU mandates, AlRiffai et al. (2010) finds, at the EU level, approximately a 9% increase in sugar crops; less than 2% increase of oil seeds and less than 0.5% increase of corn and wheat. Like our study, they also find reduction in rice and vegetables and fruits (represented by other crops in our study). Fischer et al. (2009) finds that expansion of biofuels to meet the existing targets could increase production of cereals from 2.7% to 5.4% depending on their scenarios in 2020.

Changes in agricultural trade are notable and illustrate the important role of global trade to mitigate domestic shocks in biofuel and feedstock markets. These are shown in Tables 5 and 6. All countries with biofuel targets would experience increases in total trade of agricultural commodities, mostly commodities used for biofuel feedstock and which experience the largest price changes. The increase in agriculture imports would be relatively higher in EU countries and India. The percentage increase in sugar and oilseeds imports would be relatively higher as compared to imports of other agriculture commodities. The expansion of biofuels would cause substantial decrease in exports of agriculture commodities in countries such as India, Thailand and the United Kingdom. On the other hand, export of agriculture commodities, mainly biofuel feedstock would increase in countries like Brazil, Argentina, Russia and many regions, such as Sub-Saharan Africa, MENA, and South Asia (except India). These significant two-way trade effects can be attributed to various factors. For example, the heterogeneity of the basket of commodities, such as oilseeds and grains of different quality, distance, and location of production and net surpluses and deficits areas prevailing in many countries contribute to the trade patterns. Within a large country, surplus and deficits areas can export and import more cheaply than they can access the domestic market. In the EU, intra-EU trade in oilseed is quite large with EFTA countries and Germany importing from other EU countries like France. Brazil and the U.S. extend their exports of oilseeds as they are the world’s largest soybean producers and exporters.

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users in Visakhapatnam, a port city in Andhra Pradesh situated on the eastern coast of India. Their estimated discrete choice model between formal buses and paratransit modes, including shared auto-rickshaw and three-wheeler services, indicates that females, the youth and the poor have higher preferences for paratransit. The demand for informal transport is also found to be crucially dependent on efficiency, frequency and transitability.

Still, unlike formal public transport,<sup>2</sup> it remains largely unknown in many places how the paratransit demand behaves and how the public transport sector involving informal modes could be improved or should be regulated, if needed. Taking advantage of a recent large user survey carried out in Antananarivo, the current paper attempts to examine the demand for paratransit minibus services, called “taxi-be,” within and around the city. The sample data comprise about 3,300 paratransit users. Taxi-be has the potential to contribute to reducing urban congestion and ameliorating people’s mobility if the system is better organized. On the other hand, the current chaotic taxi-be operations may even worsen the city’s congestion if proper policy measures are not taken. To better manage the growing paratransit market, it is essential to understand the demand behavior, including basic economic parameters, such as price and income elasticities.

The remaining sections are organized as follows: Section II provides a brief overview of the informal public transport sector in Antananarivo. Section III elaborates on our methodology and Section IV explains our data. Section V presents the main estimation results. Section VI examines different model specifications and discusses policy implications as well as robustness of the estimates. Then, Section VII concludes.

> 2 There are a number of studies examining the demand for induvial car use and public transportation, including rail, bus, tram, taxi and even walk (e.g., Hensher and Rose, 2007; Habibi, 2012; Deb and Filippini, 2013; Brands et al. 2014; Chakrabarti 2017).
# **5.1.2 Services and commerce**

Tables 10, 11, and 12 show the results of the analysis of our main outcome variables for the services and commerce sectors. As the first table shows, the results for the number of white-collar workers indicate that though increases are observed in both sectors as a result of increasing adoption of ICT, the magnitudes are much lower than the ones observed for manufacturing, especially for commerce. Increases in the number of blue-collar workers in the services sector are higher than those of manufacturing, while commerce exhibits very low and even non-significant coefficients in some of the specifications. Consistent with these results, for services and commerce no significant changes in the ratio of white/blue-collar workers are observed.

On the other hand, contrary to what we observe for manufacturing, wages decrease for both groups in the services sector but this reduction is higher for white-collar workers, therefore decreasing the wage gap between both groups. In this case, a 10 percentage point increase in the share of labor that uses computers is associated with a reduction of 0.24 in the wage gap between the two types of worker. This value is exactly the same as the one observed for manufacturing though the dynamics differ between these two sectors. These differences against the manufacturing sector could be explained by the fact that services appear to be comparatively more in risk of automation (though, as mentioned by Frey et al. (2016), not necessarily automated) than manufacturing (World Bank, 2016). Therefore, as routine and non-routine tasks within this sector could be automated, this reduces the skill-value of both white and blue-collar workers that currently perform these tasks, but the effect is higher on white-collar workers, therefore, reducing the wage gap.

For the commerce sector, the results regarding wages indicate that in some specifications, wages of white-collar workers appear to increase as a result of ICT adoption. On the other hand, negative effects over blue-collar wages are observed. Therefore, the wage gap between white and blue-collar workers increases. Although this sector also faces a higher risk of automation as explained in World Bank (2016), in this case the wage reduction affects blue-collar workers while the wages of white-collar workers do not change significantly. Though there is no evidence of reductions in the number of blue-collar workers for this sector (which would mean that these workers are being substituted by technology), the wage reduction could indicate that these kinds of workers are performing simpler (less valuable) tasks as the use of technologies allows to at least partially automate some processes.

34
6. In the last ten years predictability of laws and policies has increased D remained about the same D decreased [1 don'tknow O <!-- Start of picture text --> 2 0.3.<br>S 0.2<br>ID<br>0 °<br>x o.01<br>* 0.2<br>-0.3-<br>~'-0.4-<br>World LDC DC SSEA MNA CEE LAC SSA CIS<br><!-- End of picture text --> The responses show that only one region-South and Southeast Asia enjoyed an overall increase in the predictability of rules and policies. The index was measured as an average deviation from the second response to the question, which stated that the predictability of laws and policies remained about the same. According to the responding businessmen, the worst decrease occurred in the transitional economies; the Sub-Saharan Africa region and developed countries followed. In these regions the decrease was about the same in absolute terms. Small decreases in predictability were experienced by the firms in Latin America and the Middle East and North Africa. The attached chart presents another view of the change in predictability of laws and policies.

Overall only 22 of 69 surveyed countries reported an improvement of predictability in laws and policies. A country was considered having an improvement if a majority of surveyed businessmen reported that predictability increased. As it was expected from the previous graph businessmen in all surveyed Asian countries consider that over the last decade their government's policies became more predictable. This is a remarkable achievement in comparison to the poor government performance of CIS countries, where businessmen in only one out <!-- Start of picture text --> 70.<br>60]-<br>so<br>40<br>30<br>2<br>l I i<br>'4° 3 2<br>_<br>World LDC DC SSEA MNA CEE LAC SSA as<br>unter of countries wtiere predictability inremsed orrearnired the sane Enumrr of surveyed<br><!-- End of picture text --> of the ten surveyed countries think that predictability of government policies has increased. Businessmen in only four of eleven developed countries think that predictability of government laws and policies has increased.

14
<!-- Start of picture text --> Table A27: Political and Economic Risk Consultancy Corruption in Asia Survey (PRC)<br>Data Provider Political and Economic Risk Consultancy<br>Description Commercial business information firm headquartered in Hong Kong, China<br>Website www.asiarisk.com<br>Data Source Corruption in Asia<br>Type Survey<br>Respondents Expatriate business people<br>Frequency Annual since 1998<br>Coverage Asian countries<br>Public Access Commercially available<br>Description This survey asks respondents to rate severity of corruption, attitudes towards corruption, and<br>effectiveness of efforts to reduce corruption, on a 10-point scale.<br>2008 2007 2006 2005 2004 2003 2002 2000 1998 1996<br>Voice and Accountability<br>NA<br>Political Stability and Absence of Violence<br>NA<br>Government Effectiveness<br>NA<br>Regulatory Quality<br>NA .. .. .. .. .. .. .. .. .. ..<br>Rule of Law<br>NA<br>Control of Corruption<br>To what extent does corruption exist in a way that detracts from the<br>business environment for foreign companies? X X X X X X X X X X<br>Country Coverage 15 13 13 13 12 12 12 12 12 12<br>Year of publication 2008 2007 2006 2005 2004 2003 2002 2000 1998 1997<br><!-- End of picture text --> 66
# **Table A7: Marriage characteristics of currently married women, by current marital status.**

||Once married|Remarried<br>widows|Remarried<br>divorcees|
|---|---|---|---|
|Polygamous marriage|0.36|0.72***|0.47***|
|Is the first rank spouse|0.53|0.15***|0.19***|
|Co-resident husband|0.78|0.54***|0.73|
|Number of children from current union|3.51|1.53***|2.75***|
|Civil marriage|0.20|0.12**|0.18|
|In-laws living in household|0.28|0.07***|0.13***|
|Husband works in the agricultural sector|0.27|0.31|0.19**|
|Husband works in the informal (non-agri.) sector|0.37|0.33|0.45**|
|Husband works in the formal sector|0.32|0.32|0.34|
|% of woman's cell expenditures financed byher husband|0.48|0.25***|0.38***|
|N|2153|155|247|

Note: All characteristics are expressed as shares of the marital status group except for the number of children and the husband's contributions to expenditures (shares). Divorced includes separated women. All significance tests are relative to once-married women, where *** p<0.01, ** p<0.05, * p<0.1.

Source: Authors’ calculations using PSF1.

54