rafmacalaba/datause-encoder-data huggingface dataset:
intervened heavily, mostly through price-sustaining policies. These were instrumented by either government accumulation of stocks (the CCC program) or subsidizing the private sector to do that for them (the FOR program). After 1996, government policy became more market friendly by introducing price ‡oors below equilibrium and limiting the government’s budget available for stocks. Interestingly, annual changes are mostly explained by supply shocks, in contrast ith the VAR approach. When looking at overall changes through time, Figure II.A shows that no big distortions are to blame for price hikes in the 2000s. Moreover, a more detailed analysis shows that the emergence of an energy-related source of demand for corn had a signi...cant impact, and explains an important aspect of price increases in 2005-2010. This is in line with the increasing positive correlation between corn prices and energy ones. An important thing to note is that government mandates could have played a major role in this. Therefore, it could be that we are observing a shift in regulation type from direct purchases to private coercion. Identifying these interventions, quantifying them, and estimating their economic impact are subject’s for future research.

The second part of the paper analyzes the short run. It develops a quarterly based model in which agents can only produce at certain quarters, as in most agricultural markets. It then linearizes it and analyzes second order moments. When focusing on a higher frequency, there is space for speculation to play an important role, since it is possible to make pro...ts by storing for short periods of time and reselling. In other words, it is irrational for inventories to go up forever, but it is perfectly possible that they increase for one or two quarters. Therefore, the paper proceeds by modeling speculation and introduces a theoretical innovation: information shocks. Given that storage is by de...nition related to expectations about future prices, the information available at each moment is key to inventory decision-making. The model has two sources of information: public and private. Public information comes as a forecast by a government agency (in this case, the USDA). Private information comes from information markets (consulting ...rms, private research departments, etc.). These reports help agents form expectations about what future prices might be and make decisions accordingly. Given that USDA reports are publicly available, and that they contain quantitative forecasts for demand and supply, the model is able to estimate their impact on market prices. It also identi...es the precision of private signals and estimate a time series for them. Results show that as one would expect, demand and supply shocks account for a large fraction of price changes— as much as 52%. However, this leaves a relevant space for speculation or inventory 38
- Kulkarni, A.V.,I.M. Bahuguna, B.P. Rathore, S.K. Singh, S.S. Randhawa, R.K. Sood, and S. Dhar, 2007. Glacial retreat in Himalaya using Indian Remote Sensing satellite data. Current Science 92: 69-74.

- Kumar, K and J. Parikh, 2001. Indian agriculture and climate sensitivity. Global Environmental Change 11: 147-152.

Kumar, K., R.K. Dumka, M.S. Miral, G.S. Satyal, and M. Pant, 2008. Estimation of Retreat Rate of Gangotri Glacier Using Rapid Static and Kinematic GPS Survey. Current Science 94: 258-262.

- Kumar, V., P. Singh, and V. Singh, 2007. Snow and glacier melt contribution in the Beas River at Pandoh Dam, Himachal Pradesh, India. Hydrological Sciences Journal 52: 376-388.

- Lal, M., K.K. Singh, L.S. Rathore, G. Srinivasan, and S.A. Saseendran, 1998. Vulnerability of Rice and Wheat Yields in NW India to Future Changes in Climate. Agricultural and Forest Meteorology 89: 101-114.

- Lau, W.K., M.K. Kim, K.M. Kim, and W.S. Lee, 2010. Enhanced surface warming and accelerated snow melt in the Himalayas and Tibetan Plateau induced by absorbing aerosols. Environmental Research Letters 5, doi:10.1088/1748-9326/5/2/025204

- Leber, D., H. Häusler, R. Morawetz, M. Schreilechner, and D. Wangda, 1999. GLOF risk assessment in the Northwestern Bhutanese Himalayas based on remote sensing sustained geo-hazard mapping and engineering geophysical methods. Journal of Nepal Geological Society 20: 141-142.

- Liang, E.Y., X.M. Shao, and Y. Xu, 2009. Tree-ring Evidence of Recent Abnormal Warming on the Southeast Tibetan Plateau. Theoretical and Applied Climatology 98: 9-18.

- Lin, X., Y. Zhang, Z. Yao, T. Gong, H. Wang, D. Chu, L. Liu, and F. Zhang, 2008. The Trend on Runoff Variations in the Lhasa River Basin. Journal of Geographical Sciences 18: 95-106.

- Liu, X. and B. Chen, 2000. Climatic Warming in the Tibetan Plateau During Recent Decades. International Journal of Climatology 20: 1729-1742.

- Liu, X., Z. Cheng, L. Yan, and Z.Y. Yin, 2009. Elevation Dependency of Recent and Future Minimum Surface Air Temperature Trends in the Tibetan Plateau and Its Surroundings. Global and Planetary Change 68: 164-174.

- Liu, X., Z.Y. Yin, X. Shao, and N. Qin, 2006. Temporal Trends and Variability of Daily Maximum and Minimum, Extreme Temperature Events, and Growing Season Length over

38
**Table 4:  Daily Average PM10 Exposure by Age and Gender***

||Typical Hou<br>|sehold**<br>|236 Mo<br>Househ<br>|nitored<br>olds***<br>|
|---|---|---|---|---|
|Age|Female|Male|Female|Male|
|0-1|216|214|209|195|
|1-5|212|212|199|192|
|6-8|173|172|156|163|
|9-19|207|174|196|194|
|20-60|227|116|221|118|
|60+|220|161|264|188|

* Outdoor PM10 = 50

**  PM10 concentrations:  cooking area 260; living area 210

***  Averages for 236 separate calculations using monitored PM10 levels

Differences in the two sets of results stem from variations in average pollution levels, household age-sex compositions, and time allocations by household members in the full set of monitored households. We present the results for the typical household in order to display exposure variations when inside/outside pollution concentrations and individuals’ time allocations are held constant. In any case, the two sets of results are quite similar. The most striking finding is the high exposure -- around 200 -- for infants and children, regardless of gender. Exposures for student-age individuals (619) are somewhat lower (although still quite high), and also similar for both sexes. The real genderbased divergence occurs among adults, with women’s exposures nearly twice those for men in the age group 20-60, and about 40% higher for older women (over 60).

Table 4 indicates that only adult males aged 20-60 have daily PM10 exposures low enough to approach the Indian standard (100 ug/m<sup>3</sup> ). All other household members have significantly higher exposure levels, and the youngest children of both sexes have exposures that are among the most dangerous. Mortality from respiratory disease among children in this age range attests to the potent impact of such pollution levels.

Two features of our results warrant particular scrutiny. First, although attention has traditionally focused on pollution in cooking areas, our results suggest that simultaneous pollution in living areas is 12
Hirabayashi, Yukiko, Roobavannan Mahendran, Sujan Koirala, Lisako Konoshima, Dai Yamazaki, Satoshi Watanabe, Hyungjun Kim, and Shinjiro Kanae. 2013. “Global flood risk under climate change.” _Nature Climate Change,_ 3 (9): 816–21. https://doi.org/10.1038/nclimate1911.

Hourcade, Jean Charles, and Franck Nadaud. 2009. “Le prix du pétrole, les prix des carburants et nous: un regard rétrospectif.” _Collection Eclairages_ R2DS N°3 (39873): nc.

INSEE ‐ DGFiP ‐ Cnaf ‐ Cnav ‐ CCMSA. 2015. “Fichier Localisé Social et Fiscal (FiLoSoFi).” Jean-Baptiste, Nathalie, Veronica Olivotto, Emma Porio, Wilbard Kombe, and Antonia Yulo-Loyzaga. 2018. “Housing and Informal Settlements.” In _Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network_ , edited by C Rosenzweig, W Solecki, P Romero-Lankao, S Mehrotra, S Dhakal, and S Ali Ibrahim, 399–440. New York: Cambridge University Press.

Jongman, Brenden, Philip J Ward, and Jeroen C J H Aerts. 2012. “Global exposure to river and coastal flooding: long term trends and changes.” _Global Environmental Change,_ 22 (4): 823–35. <u>https://doi.org/10.1016/j.gloenvcha.2012.07.004.</u> Michel-Kerjan, Erwann, and Howard Kunreuther. 2011. “Redesigning flood insurance.” _Science,_ 333 (6041): 408–9. https://doi.org/10.1126/science.1202616.

Mills, Edwin S. 1967. “An aggregative model of resource allocation in a metropolitan area.” _The American Economic Review,_ 57 (2): 197–210.

Muth, Richard F. 1969. _Cities and Housing; the Spatial Pattern of Urban Residential Land Use._ Chicago: University of Chicago Press.

Rentschler, Jun, and Melda Salhab. 2020. _People in Harm’s Way: Flood Exposure and Poverty in 189 Countries._ World Bank Policy Research Working Paper, 9447. <u>https://openknowledge.worldbank.org/handle/10986/34655.</u> Rouchaud, D, and A Sauvant. 2004. _Prix des Logements et Coûts de Transports: Un Modèle Global d’équilibre en Ile-de-France._ Notes de Synthèses Du SES, DAEI, Ministère de l’équipement.

Su, Yichen. 2022. “The rising value of time and the origin of urban gentrification.” _American Economic Journal: Economic Policy,_ 14 (1): 402–39. https://doi.org/10.1257/pol.20190550.

Viguié, Vincent, Stéphane Hallegatte, and Julie Rozenberg. 2014. “Downscaling long term socio-economic scenarios at city scale: A case study on Paris.” _Technological Forecasting and Social Change,_ 87 (September): 305–24. https://doi.org/10.1016/j.techfore.2013.12.028.

Von Thünen, J. H. 1826. _Isolated State._ Oxford: Pergamon.

Winsemius, Hessel C, Brenden Jongman, Ted I E Veldkamp, Stéphane Hallegatte, Mook Bangalore, and Philip J Ward. 2018. “Disaster risk, climate change, and poverty: assessing the global exposure of poor people to floods and droughts.” _Environment and Development Economics,_ 23 (3): 328–48. <u>https://doi.org/10.1017/S1355770X17000444.</u> 16
Policy Research Working Paper 8962

# **Abstract**

In response to rising inequality following decades of trade liberalization, many countries are adopting trade restrictions. Can temporary trade restrictions have long-lasting effects on the spatial distribution of employment and resource allocation? To analyze this, this paper exploits the civil war in Côte d’Ivoire (2002–07), which disrupted access to the world market for two neighboring landlocked countries: Mali and Burkina Faso. The Ivorian war forced rerouting of trade from the Abidjan route to non-Abidjan routes. This paper builds a general equilibrium model where a subsistence-based autarkic hinterland coexists with an integrated segment, and there are two alternative routes to international markets. A trade shock to one route affects resource allocation in both routes by shifting the spatial margins of market integration and sectoral specialization. The effects are heterogeneous, depending on the pre-war market access of a location. The empirical analysis takes advantage of panel data and estimates the effects on structural change in employment on the non-Abidjan route using a triple difference design with location fixed effects. The areas that remain in autarkic equilibrium before and after the trade shock provide plausible estimates of the changes arising from long-term factors unrelated to the trade shock. The estimates show that the temporary trade shock created divergence between the Abidjan and non-Abidjan routes, with accelerated structural change in favor of manufacturing and services employment in the non-Abidjan route. This paper finds evidence of persistence in the effects through higher sunk investment in built-up density, agglomeration through concentration of skilled labor and greater public investment in complementary inputs such as electricity infrastructure (measured by nightlights density).

This paper is a product of the Development Research Group, Development Economics. 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/prwp. The authors may be contacted at fshilpi@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
Table 2 shows us, similar to other studies from Asia and confirming Anderson et al.’s (2017) results from rural Tanzania at a regional level, that couples commonly and systematically disagree on who is making decisions across Sub-Saharan Africa. Nearly half of couples in our DHS sample, 47.5 percent, disagree on decision-making over large household purchases. Moreover, this disagreement has a direction. The data show that the most prevalent combinations of responses are: (1) wife says decision-making is joint, but the husband says he is solely responsible (husband is “taking power” from the wife); and (2) wife says husband is responsible, but husband says decision-making is joint (husband is “giving power”). Indeed, spousal responses are significantly and systematically different in virtually all countries (21 out of 23, Appendix Table 1).

In Table 3, following specification 1, we explore the directionality of the disagreement and the association with the empowerment proxies. The scenario in which the woman takes power is clearly positively associated with more proxies of power than the case where she does not have power at all (column 1). However, column 2 shows that it is also different from the standard case in the literature of uncontested power by the woman (agreement on her power). Even though agreement on her power, which is generally positively associated with empowerment proxies, is the reference category for this outcome, we see certain positive relationships emerge. Crucially, we see that the wife taking power is more positively correlated than uncontested power with two key economic empowerment proxies: earning more than the husband and owning land. It appears that the wife taking power for herself is picking up different underlying aspects of bargaining power.<sup>6</sup>

> 6 The category of comparison, in column 2, is a restricted sample that does not include all categories of agreement. It excludes the case in which wife and husband agree that the husband is the main decision maker. This group is characterized by having a relatively larger sample of younger, less wealthy women and higher number of polygamous households, which may be driving the direction

17
# **E Construction of the Road Quality Measure**

Each discretized road segment that links two neighboring locations in our model results from aggregating a subset of actual segments. To perform this aggregation, we first use the DNIT traffic data to construct a congestion index in direction _l ∈{a_ , _d}_ , _Q_<sup>_l_</sup> ( _s_ ), for each actual road segment _s_ as where the ranking _x_ ( _s_ ) ranges between A (low congestion) and F (high congestion) depending on the capacity of the road. Then, we define the congestion index in an actual segment _s_ as _Q_ ( _s_ ) = 0.5 _×_ [ _Q_<sup>_a_</sup> ( _s_ ) + _Q_<sup>_d_</sup> ( _s_ )]. The idea is that higher congestion of traffic increases effective distance.

Once we have _Q_ ( _s_ ), we create the following network weight, _W_<sup>�</sup> ( _s_ ): where _d_ ( _s_ ) is the length of the road segment; _χP_ and _χK_ and road user-cost mark-ups for gravel relative to paved roads and secondary roads relative to other national roads, respectively; _P_ ( _s_ ) is the road pavement status; _K_ ( _s_ ) is the road category (available only for federal roads). Following Fajgelbaum and Schaal (2020) and Combes et al. (2005), we set _χP_ = 1.35, _χK_ = 1.07, and _P_ ( _s_ ) = 2 for paved roads and zero otherwise.

Following the DNIT classification, we distinguish between the following categories for federal roads: double-lane paved road, single-lane paved road in the process of conversion to double-lane road, single-lane paved road, single-lane unpaved road in the process of conversion to paved road, unpaved gravel road, and unpaved dirt road. We assume that improving road coverage by each level reduces the cost of passing through this road by 10%.

Having this information, we define the quality of a road connecting neighbors _j_ and _k_ as where 50
Second, in relative terms, the excess supply is largest for workers with the least amount of education.<sup>15</sup>

**Table 11. Excess Supply of Workers by Schooling Category, 2006**

||_s_|_is_<br>_N_|(1<br>)<br>_s_<br>_is_<br>_N_<br><br>|_is_<br>_fs_<br>_N_<br>_N_<br>|(1<br>)<br>/ (<br>)<br>_s_<br>_is_<br>_is_<br>_fs_<br>_N_<br>_N_<br>_N_<br><br><br>|
|---|---|---|---|---|---|
|**Incomplete Primary**|0.180|227171|186260|413389|0.451|
|**Complete Primary**|0.288|469525|334510|1080167|0.310|
|**Incomplete Junior High**|0.243|137191|103819|289111|0.359|
|**Complete Junior High**|0.424|673273|387796|1968788|0.197|
|**Incomplete Senior High**|0.723|289234|80233|1235346|0.064|
|**Complete Senior High**|0.776|243601|54632|1102977|0.049|
|**University**|1.000|328109|0|1834499|0|
|**Total**||2368104|1147250|7924277|0.144|

_Source:_ Authors’ analysis based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note: Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers to persons who attended and completed Junior High education but did not move on to Senior High. _Incomplete Senior High_ refers to persons who attended Senior High but did not complete it. _Complete Senior High_ refers to persons who attended and completed Senior High but did not move on to University. _University_ refers to persons who attended and obtained a University Degree. _Nis_ refers to the number of informal workers of the educational levels specified in each row. _Nfs_ refers to the number of formal workers in each of the educational levels. _(1-_  _s)Nis_ denotes the absolute size of the excess supply of workers with that level of education, and _(1-_  _s)Nis/(Nis+Nfs)_ denotes the excess supply of workers relative to the total number of workers of that educational level.

These results can be interpreted as follows: the counterfactual earnings distribution presented captures the effects that, from the perspective of workers, would be observed if they were all employed by formal firms (table 9 column 4). On the other hand, formal firms would not be willing to employ all of them in the counterfactual distribution (table 11). More precisely, firms would be least willing to employ workers with fewer years of schooling: 45 percent of workers with

> 15 Of course, if we had normalized equation (3) with, say,  1 1 , the results would show excess demand for workers of all other educational levels except, by construction, those with incomplete primary education, with excess demand larger for workers with more education. The point here is that what matters are the proportions, which reflect the fact that the isoquants derived from the production functions of formal firms are different from those derived from the production functions of informal firms.

37
# **The demography of youth in developing countries and its economic implications**

# **David Lam November 2005**

# **Appendix**

# **Appendix A.  Assumptions used in the United Nations Population Projections**

(taken from http://esa.un.org/unpp/index.asp?panel=4 on October 31, 2005)

## **ASSUMPTIONS UNDERLYING THE RESULTS OF THE** **_2004 REVISION OF WORLD POPULATION PROSPECTS_**

The future population of each country is projected from an estimated population for 1 July 2005. Because actual population data for 2005 are not yet available, the 2005 estimate is based upon the most recent population data available for each country, derived usually from a census or population register, updated to 2005 using all available data on fertility, mortality and international migration. In cases where very recent data are not available, estimated demographic trends are short term projections from the most recent available data. Population data from all sources are evaluated for completeness, accuracy and consistency, and adjusted where necessary.

To project population until 2050, the United Nations Population Division applies assumptions regarding future trends in fertility, mortality, and migration. Because future trends cannot be known with certainty, a number of projection variants are produced.

The _2004 Revision_ includes six projection variants. The results for four are available on the web. These four variants differ among themselves with respect to the assumptions made regarding the future course of fertility.

To describe the different projection variants, the assumptions made regarding fertility, mortality and international migration are described first.

**A. Fertility assumptions: Convergence toward total fertility below replacement**

41