rafmacalaba/datause-encoder-data huggingface dataset:
4

# II. **HEALTH INSURANCE IN CHINA—SOME SALIENT FACTS**

Under China’s pre-reform planned economy, almost all citizens were covered by some form of health insurance. Agricultural workers were covered by the old commune-based cooperative medical scheme (CMS), state-owned enterprise (SOE) workers were covered by the Labor Insurance Scheme (LIS), and civil servants and other government workers were covered by the Government Insurance Scheme (GIS). There were some gaps in coverage (not all urban schemes covered dependents, for example), but the gaps were relatively small (during the 1970s the CMS covered an estimated 90% of the rural population).

China’s transition from a planned to a market economy from 1980 onwards brought dramatic reductions in health insurance coverage. The decollectivization of agriculture resulted in an almost total collapse of the CMS. By 1993 less than 10% of the rural population had health insurance.<sup>4</sup> The mid-late 1990s saw several attempts to resuscitate the CMS, but despite these initiatives, CMS coverage nationally remained stubbornly low. By 2003 80% of China’s rural population—some 640 million people—lacked health insurance. In that year, half of the rural residents in 2003 who said they did have insurance said they were covered by either private (i.e. commercial) insurance or ‘other’ insurance, up from 31% in 1993.

Coverage in China’s cities has also declined, though less dramatically than in rural areas. As China transitioned to a market economy, the SOE—the backbone of the LIS—came under increasing pressure. LIS coverage fell, as did GIS coverage. By 1998 nearly half the urban population lacked insurance coverage. A variety of reforms have been introduced, including the

> 4  Figures on coverage are from China’s National Health Survey (NHS), undertaken by the Ministry of Health (MOH) in 1993, 1998 and 2003. The figures for 1993 and 1998 are published in Gao et al. (2002). The urban insurance figures for 2003 are taken from “Health Services Utilization and Urban Health Insurance Reform in China”, a presentation by Ling Xu of China’s MOH in December 2004 at an MOH seminar on the 2003 NHS. The rural figures for 2003 are from “Main Findings from the 3rd NHS Survey”, available online at www.moh.gov.cn, accessed on April 21st, 2005.
|Establish a specialized<br>body tasked with receiving<br>complaints for violations<br>of human rights (Human<br>Rights Committee, Om-<br>budsman, etc.)?|<br>Yes<br>Ombudsman<br>Act (2003),<br>Art. 2<br>Protection<br>Against Dis-<br>crimination<br>Act (2004),<br>Art. 40|Yes|Ley de la Comisión<br>Nacional de los<br>Derechos Humanos<br>(1992), Art. 2:<br>Comisión Nacional<br>de los Derechos<br>Humanos<br>Ley Federal para<br>Prevenir y Eliminar<br>la Discriminación<br>(2003), Art. 16:<br>Consejo Nacional<br>para Prevenir la<br>Discriminación (not<br>a general human<br>rights body, but<br>specialized in dis-<br>crimination)|<br>Yes|Dahir n 1-11-19<br>portant création du<br>Conseil national des<br>droits de l'Homme<br>(2011) Constitution<br>du Royaume du Maroc<br>(2011), Artt. 19, 161,<br>and 164|Yes<br>Institute for Human Rights<br>Act, Sec.1.<br>Institute for Human Rights<br>(formerly Equal Treatment<br>Commission)|<br>Yes<br>Constitution of the<br>United Republic of Tan-<br>zania (1977), Art. 129(1)<br>Commission for Human<br>Rights and Good Gov-<br>ernance Act (2001), Art.<br>6(1)(b)|<br> <br>No|
|---|---|---|---|---|---|---|---|---|
|If yes, does such body<br>hear complaints of ethnic<br>discrimination?|Yes<br>Ombudsman<br>Act (2003)<br>Art. 19 Para.<br>1<br>Protection<br>Against Dis-<br>crimination<br>Act (2004),<br>48 Para. 2|Yes|Ley de la Comisión<br>Nacional de los<br>Derechos Humanos<br>(1992), Art. 3<br>Ley Federal para<br>Prevenir y Eliminar<br>la Discriminación<br>(2003), Arts. 1 Pa-<br>ra. III, 43|<br>No|Dahir n 1-11-19<br>portant création du<br>Conseil national des<br>droits de l'Homme<br>(2011), Artt. 1, 3, and 4|<br>Yes<br>Institute for Human Rights<br>Act (2011), Secs. 3(a),<br>10(1)|Yes<br>Commission for Human<br>Rights and Good Gov-<br>ernance Act (2001),<br>Arts. 6(1)(b), 28(1)(c)|N/A|
|Is there a budget alloca-<br>tion for the operation of<br>such body/bodies?|Yes<br>Ombudsman<br>Act (2003),<br>Art. 7<br>Protection<br>Against Dis-<br>crimination<br>Act (2004),<br>Art. 40 Para.<br>3|<br>Yes|Constitución Políti-<br>ca de los Estados<br>Unidos Mexicanos<br>(1917), Art. 102<br>Para. B<br>Ley de la Comisión<br>Nacional de los<br>Derechos Humanos<br>(1992), Arts. 2, 75<br>Ley Federal para<br>Prevenir y Eliminar<br>la Disciminación<br>(2003), Art. 16 (re-<br>garding<br>CONAPRED, not<br>the Ombudsman)|<br>Yes|Dahir n 1-11-19<br>portant création du<br>Conseil national des<br>droits de l'Homme<br>(2011), Art. 55|Yes<br>The Ministry of Justice has<br>the main responsibility for<br>the Institute's budget. The<br>budget is regulated in the<br>Annual Budgets Act and<br>not in the Netherlands<br>Institute for Human Rights<br>Act.|Yes<br>Commission for Human<br>Rights and Good Gov-<br>ernance Act (2001), Art.<br>29|<br>N/A|
|EDUCATION|Bulgaria||Mexico||Morocco|Netherlands|Tanzania|Vietnam|
|Participation in Education|||||||||
|Does the law prohibit discr|imination in school admission|on ethni|c grounds in:||||||

38 Anti-Discrimination Law and Shared Prosperity
Table 1 presents differences (across a variety of dimensions) between fi rms reporting a positive number of informal workers and fi rms reporting no use of such workers in our sample. The numbers in the table are the coefficients of a regression of different fi rm characteristics on a dummy variable that equals one if the fi rm reports using informal workers. Column (1) includes no other controls in the regression, while in column (2) we control for sector dummies and some regional characteristics.<sup>20</sup> Approximately 65% of the fi rms report using some full time informal workers. For these fi rms, on average informal workers represent approximately 36% of the permanent workforce. These fi rms are on average younger and smaller in terms of total employment, physical capital, value added, and pro fi ts than those reporting no informal employment, they have a less educated workforce and are less likely to provide formal onthe-job formal training to their workers. They are also less likely to be foreign or state owned than fully legal fi rms, they tend to be concentrated in low skilled/labor intensive sectors (like clothing, shoes and wood products) and are less likely to work in chemicals, machinery and electronic products.<sup>21</sup> Finally, fi rms employing informal workers report being more likely to pay bribes to labor officials (conditional on having been inspected) and are also more likely to underreport their sales for tax purposes than fi rms only hiring formal workers.<sup>22</sup> We combine this fi rm level dataset with administrative data collected by the Ministry of Labor in 2002 on the number of regional offices that exist within each subregion<sup>23</sup> , number of inspected fi rms and the number of fi nes issued in each city (more details are given in the data appendix). We have information on the number of fi nes for different labor violations: selective.

> 20We estimate an equation of the following form: yj = βDIj + γsDs + �j, where yj is the outcome of interest, DIj is a dummy variable that equals one if the fi rm reports using informal workers, Ds are sector dummies and Zr are regional characteristics at the subregional level (total number of plants, population in 2002, log GDP in 1985, log population in 1991 and share of females).

> 21In our sample, these sectors are more likely to have a certi fi cate of quality and are more likely to develop internally and /or jointly with costumers or suppliers new technology. These sectors also have more educated workers (40% of the workers have more than the bachelor degree vs 31% for the median in the sample) and higher wages.

> 22Firms that report using informal workers are also less likely to be exporters, to have quality certi fi cation of their products and to be licensed to use foreign technology than other fi rms not employing informal workers (results not reported in table 1)

> 23The Ministry of Labor has regional offices in selected cities of each subregion. These offices are not decision units like the subdelegacias, but are designed for increasing the access of the public to the Ministry of Labor.

13
<!-- Start of picture text --> Figure 4: Municipal Business Tax Revenue for Treated and Control<br>Municipalities<br>100000<br>90000<br>80000<br>70000<br>60000<br>50000<br>40000<br>30000<br>20000<br>10000<br>0<br>Apr-07 Nov-07 Jun-08 Dec-08 Jul-09 Jan-10 Aug-10 Feb-11 Sep-11 Apr-12 Oct-12 May-13<br>Control Treatment Start of Implementation<br>End of Implementation NN Controls<br>Mean Municipal Business Tax Revenue (Reais)<br><!-- End of picture text --> 24
|:<br>Water<br>a|South<br>;<br>Africa|Upper<br>itt<br>Middle<br>Income|SSA|Middle<br>East<br>and<br>Noth<br>.<br>Africa|South<br>;<br>Asia|East<br>.<br>Asia<br>.<br>Pacific|Latin<br>‘<br>America<br>&<br>Caribbean|Europe<br>&<br>;<br>Central <br>.<br>Asia|Low<br> Income|Middle<br> Income|High<br>et<br>Income<br>.<br>OECD|World|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|SLEtoaeWaterSources<br>(% ofpopulation)|87|93|64|85|n|75|90|87|65|80|99|80|
|Access<br>to Improved<br>Sanitati<br>sees<br>fo epreverauees<br>(% ofpopulation)|67|$6|37|77|48|60|7|3%|41|7|100|64|
|[e<br>rcial Perception<br>ofWater<br>Servi<br>inn<br>hace<br>eleutamenea ee<br>Aiba! aes<br>(l=worst 7=best)|57|50|42|56|41|48|4.7|464|40|49|452|«47|
|Households UsingPipedorWellWaterasMainSource of<br>DrinkingWater<br>(%ofhouseholds)|5<br>8s|80|7<br>8|89|°0|7.<br>4|75|88|7<br>8|81||7<br>7%|

Note: Data used isfrom the latest observation available between 1997 and 2663, Source: Esiache, Antonio and Ana Goicoechea (2005). “A ‘Research’ Database on Infrastructure Economic Performance,” Policy Research Paper No. 3643, Infrastructure Network, Office of the Vice President, June 2005.
**Figure A1.4: Role of the Private Sector**

<!-- Start of picture text --> Not Consulted on Design<br>Consulted on Design<br>0% 20% 40% 60% 80% 100%<br>Private Sector Involved (No) Private Sector Involved (Yes)<br><!-- End of picture text -->

# **Annex 2: Hexagon-Level Database**

The methodology to create the hexagon-level database of nighttime lights and human settlements around transport corridors involves the following steps.

## **Step 1: Create a GIS layer of transportation corridors**

We use a tabular data set, which indicates the latitude and longitude coordinates of major nodes along the corridors of interest, to create a GIS layer representing the spatial characteristics of each corridor (including extent, form, and length). Each corridor is divided into multiple segments (figure A2.1). For each segment, we record the construction starting and end dates. The GIS layer of the corridors does not represent the exact path of each corridor along the land; it only includes segments of straight lines that connect the known nodes, a beginning and an end point of a project’s segment).

## **Step 2: Calculate buffer zones around each corridor**

We calculate a buffer layer spanning 200 kilometers around each corridor (100 kilometers on each side of the corridor). The buffer layer consists of 10 rings around each side of the corridor, in intervals of 10 kilometers (a total of 20 rings around each corridor) (figure A2.1).

## **Step 3: Create a hexagonal tessellation grid around each corridor**

We evaluate the wider economic benefits of transportation corridors by measuring changes in nighttime light luminosity around the corridors as a proxy for economic activity. To do this, rather than relying on the pixel as the unit of analysis, we calculate a Sum of Light (SOL) proxy, a well-established measure that is often used in the economic research literature to track economic development and evaluate spatial and temporal variations and social and economic dynamics. SOL is an aggregative measure that is calculated for geographical areas at different scales, for example, regions (Stathakis, Tselios, and Faraslis 2015), countries (Elvidge et al. 2014), and cities (Ma et al. 2012). Recently, studies have begun shifting from analysis at the level of the administrative unit toward analysis of artificial grid systems where each element in the grid is treated as an independent region of interest. Examples of such grids in practice include the Global Grid system (Theobald 2016) and the ISEA discrete global grids (DGGs) of Gong et al. (2013).

23
_Table 3. School exits hazard and parental income shocks, by country and level of education_

||Arge|ntina<br>P|Bra|zil<br>P|Mex|ico<br>P|
|---|---|---|---|---|---|---|
||Secondary|ost<br>Secondary|Secondary|ost<br>Secondary|Secondary|ost<br>Secondary|
|Negative income<br>|0.0508|0.147**|0.0803***|0.202***|-0.008|-0.015|
|shock<br>(quarter=q)|(0.042)|(0.061)|(0.015)|(0.027)|(0.038)|(0.053)|
|Ml|0.270***|0.167***|0.102***|0.0803***|0.155***|-0.172***|
|ae|(0.039)|(0.059)|(0.012)|(0.025)|(0.036)|(0.050)|
|Age main earner|-0.007***|-0.014***|-0.002***|-0.007***|-0.015***|-0.010***|
|(quarter=q-4)|(0.002)|(0.003)|(0.001)|(0.001)|(0.002)|(0.003)|
|Female main|0.151***|0.128**|0.029**|0.009|0.0001|-0.001|
|earner<br>(quarter=q-4)|(0.045)|(0.060)|(0.013)|(0.027)|(0.041)|(0.052)|
|Years of|-0.068***|-0.074***|-0.028***|-0.046***|-0.104***|-0.040***|
|education main|||||||
|earner|(0.006)|(0.009)|(0.002)|(0.004)|(0.005)|(0.007)|
|(quarter=q-4)|||||||
|Employed<br>|0.282***|0.392***|0.164***|0.115***|0.304***|0.358***|
|dummy<br>(quarter=q-4)|(0.066)|(0.068)|(0.014)|(0.028)|(0.045)|(0.060)|
|Quintile|-0.114***|-0.102***|-0.069***|-0.082***|-0.064***|0.012|
|(quarter=q-4)|(0.020)|(0.026)|(0.006)|(0.012)|(0.015)|(0.023)|
|Observations|26,844|13,874|60,981|25,797|43,347|21,306|
|Pseudo-R2|0.037|0.035|0.048|0.053|0.029|0.011|
|Chi-squared|1997|1134|11989|5765|762.2|136.1|
|Loglikelihood|-16400000|-7392000|-19300000|-7045000|-21098|-7870|

_Source:_ Authors’ calculations using LABLAC (CEDLAS and the World Bank). _Note:_ This table presents estimates of the relationship between schooling exit rates and a negative shock of at least 25 percent on the labor income of the household’s main earner by country. Quarter, year, and country were considered as strata. Indicators for whether the individual completed secondary or tertiary between the first and last appearance in the data are included in the specification but not reported in the table above. These two control variables are not included in the Mexico model since they are not possible to construct given the survey structure. ***, ** and * denote significance at 10%, 5% and 1% levels, respectively. Heteroskedasticity-robust standard errors are reported in parentheses.

As discussed earlier, there are important differences in the population of youth who are exposed to this risk compared to the other two countries. In particular, Mexican youth having higher exit rates before age 15, suggesting more significant “cream skimming” happening before the analysis period. Another important factor in understanding why a negative income shock may have a weaker impact on school enrollment in Mexico is the likelihood that households with Mexican youth are less credit constrained than the other two countries. First, as shown earlier, enrolled students are from higher income households, suggesting more resilience to income shocks. The specification controls for household income quintile and characteristics of the main 18
information on the geographic coordinates, along the administrative unit of the location, the day, the actors involved, the type, the number of fatalities (if any) and the description. This enables us to construct time varying state-wise conflict intensity measures by aggregating different events at the state-year level.

The main sources of information for the data come from local, regional, national and continental media. These are checked and eventually supplemented by consistent NGO reports especially in hard to access cases. In addition Africa-focused news reports and analyses are integrated to supplement daily media reporting. The result is the most comprehensive and widereaching source material presently used in disaggregated conflict event coding. As with all conflict data however caution is needed in interpreting the data as the scope for measurement error is substantial.

The calculation of consumption and production price indices is essential to the model estimation. While there are a number of surveys in Nigeria, we use the Nigeria Living Standards Survey (NLSS) 2003/2004. This is the first survey of the income and expenditure patterns of Nigerian households with sufficient data to analyze conflict over time.<sup>12</sup> Before describing the survey itself, we summarize the methodology used in calculating the price indices.

# **4.1. Price indices**

The consumption price index _CI_ for state s at time t is constructed as a geometric average of prices weighted by the budget shares (computed from the 2003/04 NLSS): where _pjst_ is the price of good j in state s at time t and _Expshr_<sup>_2003_</sup> is the share of _j_ in total expenditures in 2003/04 across households in _s_ on all the _N_ items for which price data are available. In this way the sum of the shares always equal to 1. As we can only match a subset of consumed items with prices (see table A1 in the Appendix for the list of all items matched), we scale this index by the importance of those expenditure items ( _ExpTot_ ) in total household expenditures in the state _TotExp_ (the latter term).<sup>13</sup> The main advantage of the geometric over the arithmetic average is that it allows the index to incorporate some substitution effect across commodities as relative prices change. This type of formulation is common in the literature on commodity prices and conflict (e.g. Arezki and Bruekner, 2011; Bazzi and Blattman, 2014; Calì and Mulabdic, 2014).

> 12 The Living Standard Measurement Survey for Nigeria 2010/2011 is not appropriate for the purpose of our analysis for two reasons: the survey is only representative at the geo-political zone and not at the state level, and the period 2010/2011 covers periods after the Niger Delta conflict but before the core of the Boko Haram crisis. 13 Available domestic price data will need to be matched with food and non-food items in the survey in order to estimate the indices. Items not matched will not be used in the indices but will all contribute to the weight as described.

10
However, these results must be interpreted with care.

Particularly when the secondary school enrollment ratio is sizeable, pupils in the two types of schools differ in background characteristics which, in turn, are correlated with earnings. Thus it is argued that in many countries, academic secondary schooling provides access to tertiary education and more attractive life chances than vocational schools. Competition for entry into academic schools leads to rationing of places on the basis of student academic ability: social class and parental background also play a role. Unless these factors are controlled for in comparing the earnings of general and vocational school completers, biassed estimates of the differential earnings effect will result. Studies reporting equal postschooling earnings for the two groups may be masking very real differences in labor market outcomes favoring vocational school completers.

The present paper has shown the importance of broadening the scope of these evaluation studies, particularly in relation to labor market outcomes. Too often such studies concentrate on earnings, without taking into account such intervening variables as type of occupation and its relevance to vocational studies pursued. In this case study, we have seen that such considerations are central to a proper understanding of the labor market outcomes of vocational schooling.34/ Future evaluation studies will need to pay more attention to issues of curriculum (including the type and 34 In a recent paper Hartog (1985), comparing the earnings of vocational and general schooling in the Netherlands, shows that the introduction of intervening dummy variables for job level, as additional explanatory variables in the regression analysis, changes considerably the nature of the results.

- 48 -
the importance of a wide range of “non-income” characteristics, including characteristics of the household (such as parental education, number of siblings, demographics, ethnicity, household inputs), provider characteristics (such as their quality, distance, relevance of the curriculum, gender of teachers, availability of latrines etc) and geographic characteristics (such as average consumptions and consumption inequality within the area of residence and the provision of schooling facilities). Our proposed modeling method will allow for a potentially wide range of such non-income characteristics, all of which are potential correlates of incomes, which may well account in part at least for the “macroeconomic” income effect evident in aggregate data.

While a primary aim of the paper is to provide a new methodological tool that will allow a deeper micro-based understanding of the macroeconomic determinants of HD outcomes, the results of the case studies will be of interest in their own right. The specific case studies focus on school enrollments for boys and for girls in two rather different country settings, namely Morocco and Vietnam. These countries were chosen both because of the differences in their growth and HD performances over the 1990s and because suitable survey data are available for our purposes.

# **2. Empirical model**

Our aim is to decompose differences in average schooling attainments between groups, which could be different regions or countries, different population groups such as gender or ethnicities or different dates. In setting up the decomposition we want to explicitly identify a component attributed to average consumption and one attributed to consumption inequality. To isolate the latter we need a suitably flexible nonlinear representation of how schooling varies with consumption.

4
returns are the difference between realized returns and the expected returns implied by a market model. Specifically, A A , (1) bb,tt bb,tt bb bb tt = AA −αα� −ββ̂ AARR where A A are the abnormal returns for bank _b_ at time _t_ , is the realized stock return of the bb,tt bb,tt bank, and is the market return. For each bank, and AA are the intercept and slope tt bb bb coefficients, respectively, of an OLS regression of bank AARR αα� _b_ ’s stock returns on a constant and the ββ̂ domestic market return estimated using monthly data between May 2018 and December 2019.<sup>12</sup> We compute monthly cumulative abnormal returns, which is the variation in stock prices from the last day of a month to the last day of the following month.

The empirical analysis, outlined in equation 2, consists of cross-sectional regressions that relate the monthly cumulative abnormal returns of banks to a series of bank characteristics. The dependent variable A A is the cumulative abnormal return of bank _b_ from country _c_ from month bb,tt _t-1_ to month _t_ . We refer to L L L as the bank illiquidity or liquidity risk interchangeably. bb To calculate this variable, we multiply the 2019 average cash holdings of bank LLAALL rrLLr _b_ by -1. is the bb measure of exposure for each bank to the oil sector as explained above. corresponds to the OOLLOO bb ex-ante total assets of a bank, measured in IHS. is an indicator variable that equals SSLLSSAA bb PPLLPPOOLLPP PPb rr

> 12As noted by Karolyi & Stulz, (2003), there are two alternatives in estimating the market model for a cross-country sample of stocks. The first estimates alpha and beta in the market model using a domestic market index, while the second uses a world market index. We present results using the “domestic CAPM” model and confirm most of our findings when we use the world CAPM model. We also calculate abnormal relative to a three-factor model (Fama & French, 1993), where, in addition to the market, we add size and book-to-market risk factors from the Kenneth French website.

12