rafmacalaba/datause-encoder-data huggingface dataset:
It is clear that the direction of the change in prices of domestic goods and factors cannot be detennined a priori. For example, the above discussion suggested that the excess demand for labor at initial prices would put upward pressure on the wage rate. However, if the price of domestic goods declines, as seems likely if the domestic goods are good substitutes for imports, the demand for labor will be reduced. Thus, wages and goods prices could either rise or fall in the final equilibrium. The discussion of the model does suggest several factors that are likely to affect the magnitude of the price changes. First, markets with greater levels of imports for final consumption and directly competing imported intermediates are likely to experience declines in prices, since there will be greater substitution away from domestic goods. This will also reduce the returns to owners of specific factors in these sectors, because of the decline in output price. Second, prices are also likely to fall in sectors where significant levels of imported inputs are used, because the reduction in production cost will increase supply. In these sectors specific factor owners will increase incomes, though, because the cost of inputs declines relative to the price of output. In this case imports will be a complemen **t** o domestic production rather than a substitute. Third, the impact on domestic prices is likely to be larger the greater is the elasticity of substitution between domestic goods and imported goods. Finally, the impact on prices is likely to be small when there is a significant level of exporting and a high elasticity of substitution between domestic sales and export sales.

We now turn to a parameterization of the model for the case of Chile, to illustrate how a preferential tariff reduction affects domestic prices once equilibrium has been restored in all markets.

# B. Simulation Results for Chile

The domestic production data for Chile contained information on 69 domestic production sectors for Chile. Of these sectors, 41 produce traded goods and 28 sectors produce non-traded goods.<sup>5</sup> In order to economize on the number of endogenous variables and allow the equilibrium to be calculated on an EXCEL spreadsheet, the 69 production sectors were further aggregated into a 19
**Table 6. Optimal simple rules when private agents hold stocks (quantities in million tons)**

||||**_R‐ν_**<br>|||
|---|---|---|---|---|---|
|Variables|0|3|6|9|12|
|Insulationparameter���|‐0.17|‐0.41|‐0.49|‐0.53|‐0.55|
|Storage subsidy ���|0.02|0.72|0.97|1.08|1.15|
|Share of welfare achieved by optimal<br>simple rules(%)|77.8|85.8|86.3|86.1|85.7|
|**_India_**||||||
|Meanprice(Rs/t)|9,728|9,614|9,583|9,570|9,564|
|Price CV(%)|12.77|9.91|8.53|7.76|7.16|
|Pricequantile 10%|8,232|8,632|8,818|8,906|8,972|
|Pricequantile 90%|11,343|10,954|10,769|10,644|10,544|
|Mean stock|0.08|0.48|0.95|1.31|1.68|
|Mean exports|1.09|0.73|0.62|0.56|0.53|
|Mean imports|0.01|0.00|0.00|0.00|0.00|
|**_RoW_**<br>||||||
|Meanprice(US$/t)|201.0|201.5|201.7|201.9|202.0|
|Price CV(%)|21.40|22.25|22.48|22.59|22.62|
|Mean stock|4.21|4.44|4.47|4.48|4.49|
|**_India welfare_**||||||
|Consumers' surplus|0.64|1.53|1.70|1.77|1.80|
|Producers' surplus|‐0.72|‐1.68|‐1.84|‐1.91|‐1.94|
|Total cost|0.10|0.11|0.05|0.01|‐0.04|
|Storage cost|0.00|‐0.05|‐0.12|‐0.17|‐0.22|
|Trade cost|0.10|0.16|0.17|0.18|0.18|
|Cost of volatility|0.00|0.21|0.49|0.79|1.10|
|Total welfare|0.02|0.17|0.40|0.66|0.92|

# **_6.4 Optimal simple rules of stabilization_**

The optimal simple storage policy allows for two interpretations. In the most obvious one, government provides incentives to private stock holders through the constant subsidy. Another interpretation is that the optimal storage subsidy also provides a storage rule that could be implemented by a public authority such as the Food Corporation of India, although (as noted in the calibration) storage costs are much higher for public than for private storage. For that reason, optimal storage will differ depending on whether it is undertaken by the private sector or by a public agency. The following sections seek to determine the optimal policies for both configurations, but the emphasis is on privately operated storage, given that it is much more costeffective.

# 6.4.1 Storage by private stock holders

For ����0, no weight is assigned to price volatility in social welfare. This choice does not imply that the optimal policy is free trade, because India can improve its social welfare by manipulating its terms of trade. For the trade policy, the chosen simple rule is not particularly 29
negative effect of immigrant concentration on the school performance of natives, but estimate precision suffers from the small sample size due to data aggregation.

Ohinata and van Ours (2013) use data from the 2001 and 2006 Progress in International Reading Literacy Study (PIRLS), and the 1995 and 2007 Trends in International Mathematics and Science Study (TIMMS) in the Netherlands. They use variation in immigrant concentration across classrooms within the same school to identify the effect of having immigrant classmates on natives’ test scores, and find no significant impact. Also in the Dutch context, Ohinata and van Ours (2016) use the PRIMA dataset to look at the effect of immigrant concentration at different parts of the test score distribution of native children. Using quantile regressions, they find no evidence for negative peer effects of immigrant children in any part of the distribution, after accounting for selection of migrants across schools.

Geay et al. (2013) use data on students at the end of primary school in England from 2003 and 2009. They rely on the influx of Eastern European migrants to the UK after 2005 to instrument for immigrant student concentration. They find virtually no effect of immigrant concentration in the classroom on English native speakers. Ballatore et al. (2015) use classroom formation rules in Italy as an exogenous source of variation in the share of immigrant classmates, in a sample of Italian primary schools. They find an adverse effect of the concentration of immigrant students in the classroom on natives’ test scores in both language and mathematics.

Schneeweis (2015), using Austrian primary school data, uses cohort-by-cohort variation in immigrant concentration within the same school to identify the treatment effect. She reports adverse effects of the share of immigrant classmates on the achievement of migrant students, but finds no impact on natives. Finally, Branden et al. (2016) use Swedish population registry data to investigate this question. Using a two-way family and school fixed effect estimator, they find no effect of 7
# **Table 3: Correlates of adult mortality (ln): gender and residence**

||||Sub-Sah|aran African|countries|||||Countr|ies in other|regions|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|
|Male<sup>(a)</sup>|0.186|||||||0.159|||||||
||(0.034)**|||||||(0.050)**|||||||
|Urban<sup>(b)</sup>||-0.067<br>|||||||-0.167<br>||||||
|||(0.033)*|||||||(0.050)**||||||
|Urban as child<sup>(c)</sup>|||-0.028|||||||-0.180|||||
||||(0.040)|||||||(0.055)**|||||
|Rural, Female<sup>(d)</sup>||||-0.144|-0.142||||||-0.117|-0.115|||
|||||(0.038)**|(0.034)**||||||(0.059)*|(0.051)*|||
|Urban, Male<sup>(d)</sup>||||-0.017|-0.019||||||-0.067|-0.049|||
|||||(0.038)|(0.035)||||||(0.058)|(0.050)|||
|Urban, Female<sup>(d)</sup>||||-0.242|-0.246||||||-0.393|-0.387|||
|||||(0.038)**|(0.035)**||||||(0.058)**|(0.050)**|||
|Rural as child, Female<sup>(d)</sup>||||||-0.124|-0.122||||||-0.131|-0.129|
|||||||(0.044)**|(0.040)**||||||(0.065)*|(0.058)*|
|Urban as child, Male<sup>(d)</sup>||||||-0.025|-0.023||||||-0.093|-0.083|
|||||||(0.045)|(0.041)||||||(0.065)|(0.058)|
|Urban as child, Female<sup>(d)</sup>||||||-0.172|-0.165||||||-0.367|-0.365|
|||||||(0.045)**|(0.041)**||||||(0.066)**|(0.058)**|
|Period dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|No|No|No|No|Yes|No|Yes|No|No|No|No|Yes|No|Yes|
|Observations|1312|1303|999|2485|2485|1899|1899|529|528|461|1023|1023|890|890|
|R-squared|0.32|0.36|0.30|0.32|0.44|0.27|0.41|0.43|0.42|0.41|0.34|0.51|0.33|0.49|

Note: Standard errors in parentheses. * significant at 5%; ** significant at 1%. Models include the log of GDP per capita. Left out categories are: (a) Female; (b) Rural; (c) Rural as Child; (d) Rural [as child], Male.

47
food and non-food poverty lines, equals kwacha 37,001.68.<sup>5</sup> The ELL approach combines the household survey with the Malawi 2008 Population and Housing Census (henceforward referred to as the census). The census provides individual and household level information on household composition, education, employment, dwelling characteristics and asset ownership. These data are used to obtain multiple imputed values for household consumption expenditures for all households in the census, which are then aggregated to obtain estimates of poverty at the small area level. Alternatively, the SEM approach model combines the household survey with remote sensing data, such as night time lights, urban footprints, major roads, population density, vegetation, surface temperature, rainfall. It uses these data to obtain multiple imputed values for village level (i.e. enumeration area) poverty rates which too can be aggregated to the small area level to facilitate a comparison between the two approaches. The following sub-sections provide further details on the respective databases used.

|In 2010|% population|% urban|
|---|---|---|
|Malawi|100|15.2|
|North|13.1|14.3|
|Central|42.6|15.4|
|South|44.3|15.2|

Table 1: Population in 2010

# **3.1 Household expenditure survey**

The Third Integrated Household Survey (IHS3) was conducted between March 2010 and March 2011. Its target universe consists of the households and individuals in all districts, except for the Likoma district. A total of 12,271 households were interviewed (with 668 replacements), see Table 2. The sample is designed to be representative at Statistical Office and World Bank (2012). For a more general discussion on spatial (and temporal) price deflation and real consumption measurement, see e.g. Gibson et al. (2017) and van Veelen and van der Weide (2008).

> 5The food poverty line in the IHS3 is Kwacha 22,956, the monetary cost of 2,400 calories per person per day, where the price of a calorie is estimated from the population in the 5th and 6th deciles of the aggregate distribution for consumption. The non-food poverty line (non-basic food needs) in the IHS3 is kwacha 14,045, and is estimated as the average non-food consumption of the population whose food consumption is close to the food poverty line.

13
|**Figure 22 Which Sectors Are Being Displaced?**||
|---|---|
|**a. China is displacing East Asia higher up the**<br>**technology ladder in third markets**|**b. In these products, China is itself a major market**|
|45***<br>46**<br>47***<br>48***<br>49**<br>50*<br>51***<br>52<br>53<br>54***<br>55<br>56*<br>57<br>58***<br>59<br>60<br>61***|77<br>82<br>87<br>94<br>product code<br>professional and scientific inst rument s<br>electrical machinery<br>of fice machines and automat ic dat a processing equipment|
|-3<br>-2<br>-1<br>0<br>1<br>2<br>1<br>2<br>3<br>4<br>5<br>6<br>7<br>8**<br>9<br>10<br>11<br>12<br>13***<br>14<br>15<br>16<br>17<br>18*<br>19<br>20<br>21<br>22<br>23**<br>24<br>25<br>26<br>27<br>28<br>29<br>30<br>31<br>32**<br>33<br>34<br>35***<br>36<br>37<br>38<br>39<br>40**<br>41***<br>42<br>43<br>44***<br>|0<br>4<br>8<br>21<br>25<br>29<br>35<br>51<br>55<br>59<br>64<br>68<br>73<br>to World<br>to China<br>pulp and paper|
||0<br>20<br>40<br>60<br>80<br>100<br>East Asia export growth rat e (%)|

_Source: Freund, 2006_ _Note:_ The product categories are adapted from the standard international trade classification (SITC), Rev. 3 (2-digit), United Nations Statistics Division.

**_Evidence suggests there is a relocation of production within the region._** Of the 16 2-digit China threat industries, 11 are among the top-20 list of the most rapidly growing East Asian export industries to China (see italics in table 7). This implies that some of these products—especially those in electronics (those with a standard international trade classification [SITC], Rev. 3 [2-digit] code in the 70s)—do involve fragmentation in production. So, while economies in East Asia—especially the more advanced economies—are losing trade share in third countries, they are gaining in China. China’s emergence has considerably exacerbated the triangular trade pattern by speeding up the withdrawal of the most advanced Asian economies from the production and exportation of labor-intensive products and enlarging the trade in sophisticated intermediate goods within Asia.

China is likely to maintain a long-lasting specialization in labor-intensive products. Its huge labor supply and the anticipated, substantial migration among the labor force from agriculture to industry in the coming years will maintain the country’s comparative advantage. Its gains in market share, which have come primarily at the expense of the advanced economies of the region, will provide an incentive for these economies to move up the value added chain. China will displace the NIEs in labor-intensive industries that they relinquish, as, in an earlier period, the NIEs displaced Japan in these industries. The main threat is to the less-advanced Asian economies that face China’s competition in labor-intensive products. It is not clear whether they will be able to move up the quality and technology ladder to keep a comparative edge over China.

22
- 66 -

# BIBILOGRAPHY

Ademola T. Oyejide (1986). "The Effects of Trade<sup>and Exchange Rate Policies on Agriculture</sup> in Nigeria." Research Report 55. Washington D.C.: International<sup>Food Policy Research</sup> Institute.

Balassa Bela (1988). "Incentive Policies and Agricultural<sup>Performance in Sub-Saharan Africa."</sup> PPR Working Paper No. 77. Washington D.C.: World Bank.

Bateman, Merrill, David M. Newbery, William Asenso<sup>Okyere and Gerald T. O'Mara (1988).</sup> "Ghana Cocoa Pricing Policy Paper." World Bank consultants<sup>report (processed).</sup> Bates, Robert H. (1981). Markets and States in Trogical<sup>Africa - The Political Basis of</sup> Aricultural Policies. Berkeley: University of California Press.

Bautista, Romeo M. (1988? undated). "Agricultural<sup>growth and food imports in developing</sup> countries: a reexamination," Processed. Washington, D.C.: International<sup>Food Policy</sup> Research Institute.

Berg, Robert J. and Jennifer Seymour Whitaker, eds. (1986).<sup>Strateeies for African</sup> Development. Berkeley: University of California Press.

Binswanger, Hans (1989). 'The Policy Response of Agriculture,"<sup>Proceedings of the World Bank</sup> Annual Conference on DeveloRment Economics 1989. Washington,<sup>D.C.: World Bank.</sup> Binswanger, Hans P. and Pasquale L. Scandizzo (1983).<sup>"Patterns of Agricultural Protection."</sup> ARU Discussion Paper No. 15. Processed. World Bank.

Bond, Marian E. (1983). "Agriculture Responses to Prices<sup>in Sub-Saharan AEican Countries.</sup> "International Monetary Fund Staff Papers, Vol. 20 No. 4 pp.<sup>703-736. Washington D.C.</sup> Chhibber, Ajay (1988). The Amereeate SUDPIY ResRonse in Agriculture. Forthcoming in S. Commander (ed) Structural Adjustment in Agriculture:<sup>Theory and Practice. London:</sup> James Curry Publishers.

Cleaver, Kevin M. (1985). "The Impact of Price and Exchange<sup>Rate Policies on Agriculture in</sup> Sub-Saharan Africa." World Bank Staff Working Papers Number<sup>728. Washington D.C.:</sup> World Bank.

Cleaver, Kevin (1988). "Agricultural Policy Reform and<sup>Structural Adjustment in Sub-Saharan</sup> Africa: Results to date. "World Bank Africa Department I.<sup>Washington, D.C. Processed.</sup> Delgado, Christopher L. (1987). "The Role of Prices in the<sup>Shift to Rice and</sup> Wheat Consumption in Francophone West Africa." Paper<sup>prepared for the Conference on</sup> the Dynamics of Cereals Consumption and Production Patterns<sup>in West Africa July 15-17,</sup> 1987, Dakar, Senegal: International Food Policy Research Institute.
# **Attrition Robustness—Bounds**

Table A23: Robustness of impact on SRH behavior index to differential attrition

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Main|Trimmin|g treated|Im|puting cont|rol|IPW|
||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1 (Safe Spaces)|0.20***<br>(0.08)|0.22***<br>(0.08)|0.13*<br>(0.07)|0.19**<br>(0.08)|0.20***<br>(0.08)|0.20***<br>(0.08)|0.21***<br>(0.08)|
|T1+Leaders|0.13|0.29***|-0.13*|0.04|0.14|0.14|0.14|
||(0.09)|(0.09)|(0.08)|(0.09)|(0.09)|(0.09)|(0.09)|
|T2 (Safe Spaces + IGA)|0.05|0.22***|-0.13|0.01|0.05|0.05|0.04|
||(0.08)|(0.06)|(0.09)|(0.09)|(0.08)|(0.08)|(0.08)|
|T2+Leaders|-0.02|0.29***|-0.31***|-0.09|-0.02|-0.01|-0.02|
||(0.11)|(0.08)|(0.10)|(0.11)|(0.11)|(0.11)|(0.11)|
|T3 (Safe Spaces + Husbands Clubs)|0.20**|0.39***|-0.04|0.07|0.16*|0.20**|0.20**|
||(0.10)|(0.08)|(0.09)|(0.09)|(0.09)|(0.09)|(0.10)|
|T3+Leaders|0.14**|0.20***|0.03|0.13*|0.14**|0.14**|0.15**|
||(0.07)|(0.07)|(0.06)|(0.07)|(0.07)|(0.07)|(0.07)|
|T4 (control spillover)|0.08|0.09|0.07|0.07|0.08|0.08|0.08|
||(0.07)|(0.07)|(0.07)|(0.07)|(0.07)|(0.07)|(0.07)|
|N|2855|2854|2854|2856|2856|2856|2855|
|Control Mean|0|0|0|.003|0|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. Column (1) replicates the main results presented in tables 3, 4 and 5. Columns (2)-(3) present results equivalent to Lee bounds: we re-run the estimations after dropping _K_ observations from the top/bottom of the distribution in the treatment group to calculate the lower/upper bound, where _K_ is the difference between the number of attriters in the treatment groups and the control group. Columns (4)-(6) present similar results, but instead of dropping _K_ observations in the treatment group, we impute the outcomes of _K_ attriters in the control group using the 5th, 25th and 50th percentiles of the observed distribution of the treatment group. Column (7) presents the Inverse Probability Weighting (IPW) estimates.

55
**Table 6B: Relative Mobility and Absolute Mobility Across Sons vs. Daughters**

|||**CHINA**|||**INDIA**||**IN**|**DONESI**|**A**<br>|
|---|---|---|---|---|---|---|---|---|---|
||RIGRC|RES|Linear<br>ES|RIGRC|RES|Linear<br>ES|RIGRC|RES|Linear<br>ES|
||||||Sons|||||
|No Sch|0.424|5.576|6.824|0.899|3.405|5.361|0.576|5.805|6.971|
||(0.018)|(0.173)|(0.142)|(0.009)|(0.069)|(0.049)|(0.017)|(0.160)|(0.133)|
|Primary|0.387|7.994|8.800|0.721|7.358|8.338|0.520|9.066|9.740|
||(0.016)|(0.108)|(0.097)|(0.007)|(0.039)|(0.034)|(0.014)|(0.092)|(0.085)|
|Junior High|0.376|9.137|9.788|0.664|10.798|11.315|0.506|10.605|11.124|
||(0.015)|(0.106)|(0.098)|(0.006)|(0.040)|(0.038)|(0.014)|(0.086)|(0.081)|
|Senior High|0.367|10.251|10.776|0.651|12.112|12.506|0.497|12.109|12.509|
||(0.015)|(0.122)|(0.115)|(0.006)|(0.047)|(0.045)|(0.014)|(0.100)|(0.095)|
|College|0.360|11.704|12.093|0.638|14.045|14.292|0.488|14.079|14.354|
||(0.015)|(0.161)|(0.154)|(0.006)|(0.059)|(0.057)|(0.013)|(0.135)|(0.130)|
|**IGRC**|0.329|||0.595|||0.461|||
||(0.014)|||(0.006)|||(0.013)|||
||||||Daughters|||||
|No Sch|0.638|3.169|5.057|1.180|0.232|2.974|0.694|4.547|5.964|
||(0.024)|(0.241)|(0.174)|(0.024)|(0.099)|(0.048)|(0.018)|(0.158)|(0.123)|
|Primary|0.524|6.585|7.685|0.767|4.737|6.189|0.599|8.369|9.134|
||(0.017)|(0.155)|(0.133)|(0.009)|(0.061)|(0.048)|(0.014)|(0.085)|(0.077)|
|Junior High|0.501|8.120|8.999|0.697|8.360|9.405|0.580|10.135|10.720|
||(0.017)|(0.147)|(0.132)|(0.007)|(0.074)|(0.065)|(0.014)|(0.082)|(0.076)|
|Senior High|0.486|9.599|10.313|0.685|9.741|10.691|0.567|11.854|12.305|
||(0.016)|(0.157)|(0.144)|(0.007)|(0.082)|(0.074)|(0.014)|(0.098)|(0.093)|
|College|0.476|11.041|11.627|0.673|11.776|12.620|0.557|14.101|14.419|
||(0.016)|(0.179)|(0.168)|(0.007)|(0.097)|(0.089)|(0.013)|(0.135)|(0.130)|
|**IGRC**|0.438|||0.643|||0.528|||
||(0.015)|||(0.006)|||(0.013)|||

_Notes_ : (1) The data are from children who were born between 1950 and 1989 using the CFPS 2010 round for China, the IHDS 2012 round for India, and the IFLS 2014 round for Indonesia. (2) The RIGRC refers to the Risk Adjusted Intergenerational Regression Coefficient. The RES refers to the Risk Adjusted Expected Schooling. The Linear ES refers to the Expected Schooling from a linear CEF mobility model. And the IGRC is the Intergenerational Regression Coefficient from a linear CEF mobility model.

31
# **I  Introduction**

The HIV/AIDS epidemic is probably the greatest challenge facing Africa. According to UNAIDS (2005b), in 2005, between 23.8 and 28.9 million people were infected by HIV/AIDS in Sub-Saharan Africa (this represents around 65 percent of the worldwide total and implies that 7.2 percent of adults living in that region are HIV positive), between 2.1 and 2.7 million died from the virus and between 2.8 and 3.9 million became newly infected.

It is only recently that individual level data including the results of an HIV test have become available for nationally representative samples. Previously, studies of the HIV epidemic were relying either on aggregate data or on HIV status data from nonrepresentative samples or on data from self-reported sexual behaviors. The new wave of Demographic and Health Surveys (DHS) which include HIV status now allows analyzing the socio-economic determinants of HIV infection for nationally representative samples (Akwara et al. 2005; Beegle and Ozler 2006; De Walque 2006; Lachaud 2005). This study of discordant couples uses an additional feature of the HIV data available in the Demographic and Health Surveys. The data make it possible to look at cohabiting couples (formally married or not) to assess the HIV status of both partners as well as to compare sexual behavior as reported by the man and the woman.

One limitation of the study is that, contrary to the analysis at the individual level, it excludes people who are not in a stable union, since only the characteristics and the HIV status of cohabiting partners can be found in data sets including couples only<sup>1</sup> . But the main advantage of looking at HIV status at the level of the couple is that, even in crosssections, it gives an interesting picture of the dynamics of the HIV/AIDS epidemic. In order to understand how HIV infection is spread and therefore how it can be prevented, it is important to figure out whether, if one partner is infected, the other one is almost always infected. For prevention purposes, it is also useful to investigate through which partner –male or female – the virus is more likely to enter in a couple.

> 1 Helleringer and Kohler (2006) use an interesting data set from Malawi in which they are able to track the HIV status of all sexual partners – marital as well as extra-marital – living in the same village.

2