rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Figure B1 Returns to Education of Household Head<br>.055<br>.050<br>.045<br>.040<br>2001<br>.035<br>2007<br>.030<br>.025<br>.1 .2 .3 .4 .5 .6 .7 .8 .9<br>Quantile<br>Coefficient<br><!-- End of picture text --> Figure B2 Returns to Access to Credit <!-- Start of picture text --> .35<br>.30<br>.25<br>.20<br>2001<br>.15<br>.10 2007<br>.05<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Quantile<br>Coefficient<br><!-- End of picture text --> 49
# **1. Introduction**

HIV/AIDS is the leading cause of adult mortality in Africa. In Mozambique, 12.5% of adults were HIV positive in 2008 (UNAIDS/WHO, 2008). The proportion of HIV positive patients with an advanced infection receiving HAART (Highly Active Antiretroviral Treatment) in Mozambique was 24% in 2007 (UNAIDS/WHO, 2008). Adherence to treatment is a key determinant of treatment success and is crucial in avoiding the development of resistances. Antiretroviral therapies need to be taken at least once a day and need to be taken after a meal to avoid side effects. Good nutrition is therefore an essential element in ensuring adherence and treatment success among HIV/AIDS patients. The food crisis that culminated in 2008 constitutes a major risk by reducing access to food for HIV/AIDS patients who generally are already among the most vulnerable groups in the communities. In addition, even if the antiretroviral medicines are generally subsidized, access to treatment is costly in terms of travel to the health facility and opportunity costs for patients and their households.

The aim of this paper is twofold. First, we try to evaluate the impact of the food crisis on households living with HIV/AIDS. In particular, our estimates will give an indication of whether these households are more vulnerable than others in times of economic crisis. Second, we assess whether the change in welfare of households, potentially caused by the crisis, affects their health status and adherence. We use data from a longitudinal survey carried out in Mozambique in 2007 and 2008, which was designed to include households with HIV positive individuals as well as comparison households with no identified HIV positive members.

At the global level, food grain prices have risen by 150% between January 2006 and June 2008, with about 40% of that rise that occurred in just the first half of 2008 (World Bank, 2008) _._ Ivanic and Martin (2008) have estimated that, as a consequence of higher global food prices, worldwide, the 2.3 billion people living with less then 2$ a day will become poorer, while 100 million will fall into poverty. There are multiple channels by which a food (and fuel) crisis can negatively 2
**Figure 1. Mortality concentration curve**

<!-- Start of picture text --> 100<br>0.<br>0%<br>0% lo%<br>cumulative percent of live births,<br>ranked by economic status<br><!-- End of picture text --> _Results_

**Figure** 2 shows the box plots of the six indicators by region. **Table** 2 reports the concentration indices for the health outcome variables for the 42 countries, along with averages for the sample of countries and three regional groupings-Asia, the Near East and North Africa (AsiaMNA); Latin America and the Caribbean (LAC); and Sub-Saharan Africa (SSA).

**Figure 2. Medians, ranges, intorquartile ranges and outliers of concentration indices**

<!-- Start of picture text --> .6<br>4*Uzbekistan<br>.4_<br>.2<br>-XUzbekistan<br>Oazakhstan<br>T -CIIMR<br>o LIT_1El T d 3 [OCI lfor u5t.IR<br>-E 2 1 j~~~Tm OB azit N b i' CILOWHFA<br>1T-~ T a ' CILOWWFA<br>-.4 1 j CIDIA<br>-.6 .. F._ CIARI<br>N 13 13 10 10 1313 8 8 8 8 8 8 21 21 20 20 21 20<br>AsiaMNA LAC SSA<br>REGION<br><!-- End of picture text --> 6
||s-era5-single-<br>levels?tab=overview|||https://earlywarning.us<br>gs.gov/fews/product/1<br>28|||
|---|---|---|---|---|---|---|
|Data set<br>reference/citati<br>on|Hersbach, H., B. Bell,<br>P. Berrisford, G.<br>Biavati, A. Horányi, J.<br>Muñoz Sabater, J.<br>Nicolas, C. Peubey,<br>R. Radu, I. Rozum, D.<br>Schepers, A.<br>Simmons, C. Soci, D.<br>Dee, and J.-N.<br>Thépaut. 2018.<br>“ERA5 Hourly Data<br>on Single Levels<br>from 1979 to<br>Present.” Copernicus<br>Climate Change<br>Service (C3S) Climate<br>Data Store (CDS).|Funk, C.C., P. J.<br>Peterson, M. F.<br>Landsfeld, D. H.<br>Pedreros, J. P. Verdin,<br>J. D. Rowland, B. E.<br>Romero, G. J. Husak, J.<br>C. Michaelsen, and A.<br>P. Verdin. 2014. “A<br>Quasi-Global<br>Precipitation Time<br>Series for Drought<br>monitoring.” US<br>Geological Survey Data<br>Series 832, ftp://chg-<br>ftpout.geog.ucsb.edu/<br>pub/org/chg/products/<br>CHIRPS-<br>2.0/docs/USGS-<br>DS832.CHIRPS.pdf.|Sergio M. Vicente-<br>Serrano, Instituto<br>Pirenaico de Ecología,<br>Zaragoza, Spain.<br>Santiago Beguería,<br>Estación Experimental<br>de Aula Dei, Zaragoza,<br>Spain.|Verdin, J., and R. Klaver.<br>2002. “Grid-cell-based<br>Crop Water Accounting<br>for the Famine Early<br>Warning System.”<br>_Hydrological Processes_<br>16 (8): 1617–30. DOI:<br>10.1002/hyp.1025.|Copernicus Service<br>information 2018.|Didan, K. 2021.<br>MODIS/Terra<br>Vegetation Indices<br>Monthly L3 Global<br>0.05Deg CMG V061<br>[data set]. NASA<br>EOSDIS Land<br>Processes DAAC.<br>Accessed 2021-06-24<br>from<br>https://doi.org/10.50<br>67/MODIS/MOD13C2.<br>061.|
|Additional<br>information/<br>notes||||Range of WRSI values:<br>< 50: Failure<br>50–60: Poor<br>60–80: Mediocre<br>80–95: Average<br>95–99: Good<br>99–100: Very Good<br>0: N/A<br>The following values<br>are flags (and not<br>specific WRSI values in<br>the normal range 0–<br>100):<br>253: No start (late)<br>254: Yet to start|||

39
average trade flows over this four-year period. To improve the matching between online and offline flows we only look at eBay exports by businesses, and we ignore all imports purchased via auctions, which are prevalent on eBay but quite uncommon offline.<sup>5</sup> Our dataset also allows us to focus on the same goods traded online and offline. It covers all eBay transactions disaggregated into 40 product categories that we match with product codes at the 6-digit level of the HS classification using information on sub-categories from the eBay website (see our matching table (9)). Since it is impossible to match some eBay categories to HS codes, we dropped those goods from our eBay aggregate. This allows us to have an offline basket with the same goods, similarly distributed across categories, as our eBay trade flow (Figure 3). It is also important to note that the selected HS categories all fall into the “final good” category of the WTO’s Trade Policy Review classification, and are all classified as “consumer goods” in the BEC classification. All HS 6-digit lines also fall in the differentiated goods category in Rauch’s (1999) classification.

The matching of goods is crucial as it allows us to control for differences in trade costs due to the composition of trade.<sup>6</sup> For example, tickets to sport-events traded online are likely to be very sensitive to distance whereas exports of rare earths, which are produced in a few countries but consumed all over the world, are not likely to be very sensitive to distance. If tickets tend to be traded online and rare earths offline, differences in the impact of distance will be explained by the different goods, and not by information and communication technology.

To verify whether our product matching is correct, we estimate the elasticity of substitution associated with our baskets of goods online and offline. This step is important as different elasticities of substitution could also be behind the difference in distance effects (see Archanskaia and Daudin 2012). Indeed the coefficient in front of each trade-cost variable in the gravity equation is a combination of the trade elasticity (i.e. the elasticity of trade with respect to trade costs) which depends on the elasticity of substitution, and the elasticity

> 5The share of sales by consumers is 66 percent and the share of sales through auctions is 65 percent. Once we exclude both, we are left with 15 percent of total eBay’s cross border flows.As we show in our robustness checks, results hold when including all flows.

> 6See Berthelon and Freund (2008) or Carr`ere et al. (2009) for a discussion of the impact of the composition of trade on the role of distance.

6
the larger sample used for this measurement compared to the Matched sample. However, depending on the circumstances in which the projections are made any one of the three measures may be used.

# 3.2.5. Remapping US Facilities to 4-digit ISIC

Having matched the TRI data to the LRD information at the facility level, it was necessary to select a suitable level of aggregation of industrial activity for international comparisons of pollutant intensity. The 4-digit ISIC level, comprising about 80 sub-sectors, was selected, since it is the most detailed and comprehensive level of reporting used byUNIDO.5 A standard US Department of Commerce concordance was used to assign a 4-digit ISIC code to each sector. Difficulties arose in dealing with those facilities reporting under more than one 5- digit SIC code when the facility's SIC codes matched more than one ISIC classification. The standard procedure for dealing with this problem was to assign each facility the 4-digit ISIC code with the greatest shipment value. Although this was generally 80% or more of the total shipment value, this approach inevitably lent some inaccuracy to the final estimates of pollutant intensity.

5pollution intensity estimates were also derived for other levels of disaggregation: 2-digit, 3-digit and 4-digit US Standard Industrial Classification (SIC) sectors, which have respectively 9, 39, and 1500 sub-sectors.

18
<mark>As there is no reliable labor data (such as wage and labor supply) in the market jobs, I cannot directly measure the reservation wage. The majority of households which were engaged in informal jobs (in agriculture, trading, and service sector) were not wage labor. As a result, I cannot estimate the shadow wage from the production function. As an alternative, I impute the average shadow wage of non-fishing job at each village using the following step:</mark>

- (1) <mark>Regress the real per capita household income in 2007 on a dummy on fishing, village</mark>

<mark>dummies, and their interaction terms. Obtain the predicted shadow wage of non-fishing households which differs across villages.</mark>

- (2) <mark>Take the average of the predicted per capita income w�</mark> ıvk <mark>to obtain the average reservation wage at each village.</mark>

<mark>Table A.1 shows that the negative effects of</mark> _<mark>AGGSHOCK</mark>_ <mark>on fishing in 2007 (robustness check for Table 5) remain to be robust when using any proxy variables and the imputed reservation wage. People living further away from Banda Aceh tended to be engaged in fishing, and they were likely to opt out from fishing if higher reservation wage was offered in the non-fishing sector. The main results in Table 7 and Table 9 are also shown to be robust.</mark>

# **References**

Jacoby, H. (1993) Shadow wages and peasant family labour supply: an econometric application to the Peruvian Sierra. _Review of Economic Studies_ . 60(4), pp. 903-921 43
International Student Assessment (PISA), and the International Association for the Evaluation of Educational Achievement’s Trends in International Mathematics and Science Study (TIMSS) and Progress in International Reading Literacy Study (PIRLS) have played key role in cross-country and within-country studies of these determinants (for example, Hanushek and Luque 2003; Hanushek and Kimko 2000; Barro 2001; Lee and Barro 2001; Afonso and Aubyn 2006; Bedard and Ferrall 2002; Hanushek and Woessmann 2005; Alvarez, Garcia-Moreno and Patrinos 2007; Nabeshima 2003; Fertig 2003; Fertig and Schmidt 2002; Woessmann 2003; Fuchs and Woessmann 2007; World Bank 2005a, 2006; Parker et al. 2008).

The purpose of this paper is to measure, comparably across countries, the extent of wealth and gender inequity in cognitive skills among youth and to identify the general sources of these inequities. Being able to compare the extent of these inequities across countries as well as understanding their general sources will help guide policy makers intent on eliminating it.

To measure inequity, this paper borrows a concept from wage earnings literature. In this literature, gender (or racial) inequity is quantified by the size of the unexplained difference in earnings between males and females which emerges after the difference explained by observable characteristics such as educational attainment, etc, is removed (see for example Oaxaca, R. 1973; Blinder, A. 1973). In other words, the extent of gender inequity in wages equals the extent of wage dependence on gender. An analogous quantification applies to cognitive skills: the extent of wealth and gender inequity in 3
2005 (Park and Cai, 2011) and 29% by 2011.<sup>5</sup> Presumably the missing workers lacked labor contracts (they were not reported by their employers or were unregistered self-employed workers) as did the 10% registered self-employed. One Ministry of Labor survey, conducted in 66 cities in 2002, and primarily sampled local resident workers, estimated that 45% of urban workers in China were informally employed.<sup>6</sup> According to the 2005 China minicensus, 52% of urban workers lacked contracts, including 50% of local residents, 69% of rural migrants, and 38% of urban migrants. Park and Cai (2011) provide evidence suggesting that many informal workers are rural migrants, or are working in the private sector or service sector jobs – three groups that have grown rapidly as a share of the urban labor force.

Did the 2008 Labor Law help reverse these trends? Using multiple waves of the China Urban Labor Survey, we make consistent comparisons of the prevalence of labor contracts over time. Figure 1 summarizes the main findings. The share of local resident and migrant workers with a labor contract actually fell from 2001 to 2005, declining from 67% to 65% for local resident workers and from 15% to 12% for migrant workers. But this trend toward greater informality reversed by 2010, when 71% of local resident workers and 34% of migrant workers had labor contracts. The increase by more than 20 percentage points for migrant workers is notable. Thus, the data suggest that the trend towards greater informalization in China reversed itself between 2005 and 2010, and quite sharply for migrant workers.

A more detailed breakdown of these trends is provided in Table 4, which also reports results separately for wage employees (excluding the self-employed), and breaks down the

> 5 Author’s calculations from employment figures published in the _China Statistical Yearbook 2012_ (National Bureau of Statistics 2012).

> 6The following types of workers were categorized as informal: 1) hired workers without formal contract not listed as formal employees; 2) domestic workers, temporary agency workers, and casual laborers; 3) community service workers without formal contract; 4) workers hired on the basis of hourly pay, daily pay, weekly pay, and uncertain pay (in terms of time and/or account); 5) paid helpers in family and self-employed businesses; 6) workers hired by individual entrepreneurs; 7) individual business owners ( _getihu_ ), note that the ILO recommends that small business of less than 10 workers be considered informal, in China individual businesses are those with 7 or fewer workers. Workers in the agricultural sector are excluded in the analysis.

12
**Table** 14: **Share of different sectors in the total GDP (in percent).**

|Sectors|1988|1989|1990|1991|1992|1993|1994|
|---|---|---|---|---|---|---|---|
|Agriculture|49.6|49.0|47.9|48.6|48.6|47.8|47.3|
|Agriculture & livestock|34.9|33.2|33.3|33.9|33.5|33.1|33.0|
|Cocoa production & Mktg.|8.8|9.7|9.1|9.5|9.8|9.3|9.0|
|**Industry**|16.6|16.7|15.9|16.0|16.2|16.0|16.2|
|Mining|2.0|1.9|1.8|1.8|1.9|2.0|1.8|
|Manufact.|9.6|10.0|9.2|8.7|8.7|9.1|9.0|
|Construction|2.9|3.0|3.1|_3.5_|3.5|3.2|_3.3_|
|**Services**|33.6|33.4|35.8|35.3|34.9|35.7|36.1|
|transport,storage &Comm.|4.2|4.3|4.4|4.5|4.4|4.3|4.4|
|wholesaleandretailtrade|18.9|18.7|19.0|17.2|18.3|19.0|19.2|
|_Finance_and insurance|2.7|2.7|3.9|4.2|3.6|3.8|3.8|
|Govt.services|6.9|6.9|7.5|8.2|7.5|7.5|7.5|
|Community, social services|0.7|0.8|1.0|1.1|1.0|1.0|1.1|
|**Total**|**100.0**|**100.0**|100.0|100.0|100.0|100.0|100.0|

# **Source: Ghana Statistical service, Ghana.**

The trends in the real average monthly earnings per wage worker in the public and private sector are shown in Figure 3. It is apparent from the graph that the real earnings have some resemblance to the rate of growth of GDP over the years. The dominant picture is that of a steady increase in real earnings in the early l 980s with a somewhat decline in the late 1 980s, more so in the public sector than in the private sector (Fig 3). While the real earnings in the private sector have remained flat in the 1980s, the same in the public sector have been very unstable, with the real earnings having fallen in the late 1980s. Does the fall in earnings in the formal sector imply that the large wage differential in favor of the formal sector observed in the 1 970s and early 1 980s has largely disappeared. In other words is the labor market closer to an equilibrium situation in which the formal sector wages are more in line with the supply price or alternate earnings of labor? This question has to be addressed in terms of the urban informal and rural sectors.

Another related issue is whether the fall in real earnings was caused by a fall in real wages or by a fall in employment or both. The data analysis tells us that there has been a fall in employment in the formal sector, but we don't have the wage information of the self-employed. Similarly, secondary data (Ghana Statistical Yearbook) also provides the evidence that there has been a fall in overall employment, more so in the public sector (Fig 4). Thus, we can say that fall in employment was one of the factor resulting in the fall in real earnings. The effect of the trend of real wages on the trend of real earnings also depend on the wage determination process in the formal sector-through unions or the government. Government labor market policies either through minimum 26
# **Table 6. The Effect of Drought Relief Compensation on Turnout 2006**

||Dependent variable: Total number of votes<br>casted,2006|
|---|---|
||(1)|
|Below Cutoff|-55.228<br>(37.058)|
|Rain Deviation|0.028<br>(0.426)|
|Constant|399.184<br>(328.255)|
|Observations|1038|
|R-squared|0.2251|
|State controls|Yes|
|Controls at electoral section level|Yes|
|Controls at municipal level|Yes|
|Mean of dependent variable|664.51|

Robust standard errors in parentheses State controls are dummy variables for each state. Controls at the electoral sections include altitude, distance from the weather station, distance to the nearest river and distance to the "cabecera". Controls at the municipal level include municipal income per capita for the year 2000. * significant at 10%; ** significant at 5%; *** significant at 1% Page **28** of **29**
literature that investigates the concept of resilience in economic development (see, e.g., Barrett and Constas, 2013).

Our approach is related in spirit to other methods of measuring vulnerability as expected poverty (e.g., Chaudhuri, 2003) as well as of identifying the middle class, as recently proposed by Lopez-Calva and Ortiz-Juarez (2014). These two studies share certain common features such as employing a number of household variables in their parametric models, but generally follow quite different approaches. The former works on cross sections but make rather restrictive assumptions (including the estimated coefficients on household characteristics being fixed over time and the inter-temporal variation of household consumption being represented by its crosssectional variability), while the latter relies on true panel data for its estimates.<sup>6</sup> Notably, besides the general contributions discussed above, our method attempts to improve on the limits in these studies. Our approach is non-parametric, makes much less use of panel data, and have more parsimonious assumptions and fewer intricacies of estimation. Our method is thus simpler but can provide results that are comparable across different countries. This simplicity also permits application of the method to synthetic panels built up from two or more rounds of cross-section data. Since nationally representative panel data sets are quite scarce, particularly in the developing world, while “snap-shot” cross-sectional surveys are far more common, our approach thus offers a means to estimate vulnerability lines in a great number of settings where panelbased methods would not be applicable.

We provide empirical illustrations of vulnerability lines derived on the basis of both true panel data and synthetic panel data from three countries at differing income levels and from different geographic regions—India, the United States, and Vietnam. Our estimation results

> 6 S ee Hoddinott and Quisumbing (2010) for a more detailed discussion of the other assumptions made in Chaudhuri (2003).

6
# **Table 3: The socio-economic determinants of mortality in 1994 in Rwanda, using age at marriage as a proxy for ethnicity**

|**Dependent variable: Died in 1994**<br>**A. Education is measured by comparing primary education with no education**<br>(1)<br>(2)|
|---|
|Male<br>0.018<br>0.013|
|[0.006]***<br>[0.005]***|
|Sister with primary education<br>0.024<br>0.014<br> <br>|
|[0.010]***<br>[0.009]|
|Sister was living in an rural area in 1994<br>-0.022<br>-0.036<br> <br>|
|[0.010]**<br>[0.008]***|
|Never married<br>-<br>0.069|
|[0.032]**|
|Age at marriage if married<br>0.003<br>0.003|
|[0.001]**<br>[0.001]**|
|N =<br>31523<br>44568|
|**B. Education is measured by comparing secondary education with lower schooling**<br> <br> <br>|
|Male<br>0.019<br>0.013|
|[0.006]***<br>[0.005]***|
|Sister with secondary education<br>0.045<br>0.040|
|[0.020]**<br>[0.016]***|
|Sister was living in an rural area in 1994<br>-0.022<br>-0.033|
|[0.010]**<br>[0.008]***|
|Never married<br>-<br>0.061|
|[0.031]**|
|Age at marriage if married<br>0.003<br>0.002|
|[0.001]**<br>[0.001]*|
|N =<br>31523<br>44568|
|**C. Education is measured by the years of education**|
|Male<br>0.019<br>0.013|
|[0.006]***<br>[0.005]***|
|Years of education (of sister)<br>0.004<br>0.003|
|[0.001]***<br>[0.001]**|
|Sister was living in an rural area in 1994<br>-0.020<br>-0.034|
|<br>[0.010]**<br>[0.008]***|
|Never married<br>-<br>0.061|
|[0.032]*|
|Age at marriage if married<br>0.003<br>0.002|
|[0.001]*<br>[0.001]*|
|N =<br>31496<br>44522|

Marginal effects of logit regressions, dummies for age in 1994 included. The data is taken from the siblings' mortality data of the Demographic and Health Survey, 2000, Rwanda. The data are weighted as recommended by the data provider, and adjusted to account for unequal probability of selection into the sample by a factor of 1/number of surviving females age 15-49 in each family. Where more than one respondent in the same household reports on the same sibship, only one respondent is randomly retained. The background of the family is derived from the characteristics of the sister who answered the survey. For sisters who never moved or moved for the last time before 1994, the actual location is recorded. Sisters who moved after 1994 are assumed to have been living in 1994 in the same type of location as the one reported, in 2000, as the previous one. Clustered standard errors between square brackets. ***, **, *: significant at the 1, 5 and, 10 percent confidence interval, respectively.

20