rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 13: Beneficiaries of private health insurances, by quintile**

<!-- Start of picture text --> 0.8<br>0.7<br>0.6<br>Q5<br>0.5<br>Q4<br>0.4 Q3<br>Q2<br>0.3<br>Q1<br>0.2<br>0.1<br>0<br>2008 2009 2010<br>Source: Bank staff calculations, ENFT survey<br>% enrolled in private health insurances<br><!-- End of picture text --> How has private health affiliation varied over the past three years? Much alike the case of private schools, enrolment in private insurances decreased in 2009, to quickly rise again in 2010, especially among the middle and high class. Quintile 4 and quintile 5 present also a higher level of affiliation to private health insurances, and they are the only significant quintile dummies when a logit regression is applied to test opting for private health insurances across quintiles. As can be seen in the graph, differences across quintiles in private health affiliation are much wider that in the case of private education.

We also include a dummy for informal workers; against the intuition, formal workers show more inclination to sign up in private health insurances than informal ones. It seems that informality levels are lower among middle and high class employees, groups that usually have the purchase power to acquire private insurances, and they prefer to do so, since public services have relatively low quality standards.

To sum up, the Dominican Republic Social Insurance System is still recent and the ambitious set of reforms reforms aimed at expanding its coverage and improving its quality has not been fully implemented yet. Additionally, it can be argued that the system is “fragmented” between people in the contributory and subsidized regimes. These are some of the reasons why the upper intermediate strata of the
represents the short-term flexibility of the production system with inventories, while CGE models (e.g., Rose et al., 2005) use constant-elasticity substitution depending on price levels.

# _3.2 Data and parameters_

The economic data is the same than in H08, with two major differences. First, as already stated, the housing service is now included in the table. Second, new estimates of the amount of productive capital in each sector have been produced. In H08, a unique ratio of sector productive capital to sector value added was used, in all sectors. In this new version, US-scale data on the amount of private and government capital, from the Bureau of Economic Analysis, are used to assess national sector-scale ratios of productive capital to value added. These ratios are then used for Louisiana, assuming that the structure of each economic sector is similar in Louisiana and in the US as a whole. These ratios are reproduced in Tab.1.

No data are available on the distribution of damages to productive capital among sectors. The Congressional Budget Office produced estimates in broad categories: 17 to 33 billion US$ in housing; 5 to 9 US$ in consumer durable goods; 18 to 31 US$ in the energy sector; 16 to 32 US$ in other private sector businesses; 13 to 25 US$ for the government. Taking the conservative side of these estimates, these losses were distributed among the different sectors according to their respective size and other information, to assess productive capital losses in all sectors. Housing losses and consumer durable goods were included in the financial and real estate sector; Energy sector losses were distributed in the utilities and mining and extraction sectors ($5 billion and $15 billion, respectively); Government losses were distributed in the energy, transport, and government sectors; Other private-sector losses were distributed in all other sectors, following their respective productive capital. All losses are reproduced in Tab. 1. Aggregated direct losses amount to $63 billion, and are
Uttar Pradesh, and Maharashtra (Thangaraj 1995). Of the more recent programs, Maharashtra has made less progress than Uttar Pradesh, perhaps because in Maharashtra soils tend to be more heterogeneous and because the extent of fragmentation was lower to begin with, thereby reducing effective demand for consolidation from farmers. Some work towards administrative land consolidation has been carried out in Madhya Pradesh, Gujarat, Kamataka, Andhra Pradesh, Bihar, Orissa (Mearns and Sinha 1998) and Rajasthan, and almost none in West Bengal and Assam, in spite of the fact that supporting legislation has already been enacted. In South India, it is suggested that there may be less to gain from administrative land consolidation because the extent of fragmentation is lower, voluntary consolidation has more easily been achieved, or because there are sound ecological reasons for its persistence (cf. Farmer 1960). Notably, Tamil Nadu and Kerala made no legislative provision for land consolidation programs, but in both states spontaneous consolidation has been possible through the land market (Thangaraj 1995). Everywhere, however, the high transaction costs of land transfers (discussed below in relation to records of rights in land) are significant constraints on voluntary consolidation of ownership holdings. Overall, it appears that measures to deregulate land markets may indirectly result in more land consolidation than would further administrative efforts to implement land consolidation programs directly.

# GENDER **AND LAND RIGHTS**

No discussion of constraints on access to land for socially excluded groups in India can be complete without considering the lack of effective, independent land rights exercised by women. A focus on rural poverty reduction alone will fail to uncover the full extent of the social exclusion of women in gaining access to land, whether through inheritance, through state land redistribution, or through the market. Female workforce participation rates, rather than property rights, are widely used as the main indicator of women's economic status in India (Agarwal 1995). But while better employment opportunities may complement land-based opportunities, they cannot substitute for land. Efforts
ultimately translate into higher household expenditure (both food and non-food), as well as human capital investment (in children, for example). The results show that the returns to road investment for household per capita expenditure are about 11 percent in the RDP and RRMIMP villages, a substantial gain in terms of higher consumption and income for rural households. This means that rural households in villages targeted by the road development project have on average an 11 percent higher consumption per capita per year.<sup>20</sup> In addition to household consumption, average school participation among boys is about 20 percent higher in RDP villages, not controlling for proximity to the project road; road development also has a positive but barely significant impact on girls’ schooling in the same sample. In the RRMIMP data, the project effect is fairly similar for boys and girls, leading to a 14 percent increase in schooling. Indeed, in our results only the project indicator seems to have had any significant impact on schooling in the RRMIMP sample. Higher school attendance of boys and girls are therefore some of social returns due to road investment; additional social returns to be assessed from road investment include returns to health and nutrition, which are not pursued in this paper.

Finally, as a robustness check, we estimate the non-price outcomes (HH per capita expenditure, Laspeyres quantity index, boys’ and girls’ schooling, as well as male and female labor supply) on the same set of explanatory variables as well as the price variables discussed above (men’s agricultural wage, fertilizer price, and the agricultural price index facing the household). The net returns are presented in table 3, and reflect the returns if we held the structural model (taking prices as exogenous) to be correct. Overall, the estimated effects are slightly higher than our original returns, except for the aggregate output index where the return is lower. The results are fairly robust and in almost all cases retain significance with our earlier estimates.<sup
-111-

# Table 3 (Brazil)

In Brazil there is no analytic coding in the census. The detailed information on quantities and values by product in the volume Produc&c Fisica of the Censo Industrial: Brasil (IBUE, 1981b) is listed with a sequence of numbers of 1 to 13.678. Some of these numbered items refer to production in Brazil as a whole and others to production by state. The summary volume Censo Industrial (IBGE, 1981a) gives an analytic breakdown of gross value of output, census value added, employment and inputs for 24 branches of industry, of which 23 are manufacturing branches. The numbering system is this summary volume is different from that in Producao Fisica, though the same sequence of branches is used in the two volumes.

# Table 4 (Mexico)

The MN Ican census is issued in two main volumes for Mexico as a whole, the Desgloae (SPP, 1979b) which only gives output quantities and values, and the analytic summary volvue Resumen General (SPP, 1979a) which gives information on employment and inputs as well. Mexico has its own four digit code (Catalogo Mexicano de Actividades Economicas) for 239 industries, but the items within each branch are not numbered. They aere arranged instead in descending order of the gross value of shipments. The degree of product detail in the Mexican census is generally bigger than in the US and much bigger than in Brazil. Sometimes a product with the same name is listed in more than one branch. In rarer cases one may find the same product mentioned twice within a branch.

# Tables 5. 6 and 7

The binary matchings which we made to derive PPPs for gross output are presented in tables 5 (US/Brazil), 6 (US/Mexico) and 7 (Mexico/Brazil). Before matching all quantities of measurement were expressed in metric units, e.g. US short tons were converted to metric tons and US gallons to litres. Tables 8, 9. 10 These
as well as the aggregate effects on TFP. These results are important since they imply that only the behavior of certain types of firms is distorted by weak contracting institutions through the family management channel. Our model also relates to Caselli and Gennaioli (2013) and Akcigit, Alp and Peters (2016) who calibrate dynamic models of family management induced by a weak contracting environment. While our model is also general equilibrium, equilibrium can be solved in closed form which allows us to parsimoniously derive predictions characterizing the sorting of firms across management modes and the effects of contracting institutions on firms’ managerial choices. The model also yields a simple and intuitive sufficient statistic for aggregate productivity that depends on the share of professionally-managed firms in the economy.

Third, we show that the patterns in the data are consistent with the predictions of the model to show how contractual institutions relate to the incidence of family firms and aggregate performance. In contrast to the existing literature that examines cross-country correlations between family firms and legal institutions (e.g. La Porta, Lopez-de-Silanes and Shleifer 1999, Burkart, Panunzi and Shleifer 2003, Aminadav and Papaioannou 2018), we are able to leverage our large sample size to examine how management modes respond to changes in proxies for the contractual environment across regions within countries. Given the lack of sub-national measures of rule of law or contracting institutions, our analysis centers on using withincountry variation of the World Values Survey measure of trust as a measure of enforcement of social norms

> 7In parallel work, Aminadav and Papaioannou (2018) draw on Orbis to generate data on 40,000 listed firms in 127 countries and explore determinants of a wide variety of types of corporate control in listed firms in 127 countries. This enables them to to trace controlling shareholders from the often obscure corporate structures and identify large differences in types of corporate control across and within continents and correlate these structures with legal traditions. Our paper differs in its focus on family management, using regional variation in trust to examine the effects of institutions on firm management choices
**Figure 11:  Cargo Dwell Time in Select African Countries, by the Percentage of Containers, 2011**

<!-- Start of picture text --> 0-5 6-10 11-20 21-40 41-70 >70<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Kenya Nigeria South Africa Uganda Zambia<br><!-- End of picture text --> _Source:_ Authors’, based on firm surveys.

# **Comparison with Shippers’ Perception of Dwell Time**

When importers’ perceptions of cargo dwell time are compared with actual dwell time in Sub-Saharan African countries, the latter is higher than expected in Kenya and Nigeria, which means that importers here are likely to want to reduce dwell time and might exert pressure to do so.

On the contrary, in South Africa, Uganda, and Zambia, the perception of normal cargo dwell time is higher than the estimated normal dwell time, meaning that there may not be strong pressure to lower it; importers might be satisfied with experiencing a shorter dwell time than expected. In both cases, average dwell time is lower in Durban than in the other ports studied, which may explain the relatively low expectations.

These differences between countries suggest that dwell time is also about perception and information (Table 1). Shippers may not understand the significance of dwell time and may not have accurate information on it (dwell time statistics are often unknown and incorrect).

**Table 1: Average Dwell Time and the Perception of Normal Dwell Time in Select African Countries, 2011** _dwell time (number of days)_

|_Indicator_|_Kenya_|_Nigeria_|_South_<br>_Africa_|_Uganda_|_Zambia_|
|---|---|---|---|---|---|
|Port|Mombasa|Lagos|Durban|Mombasa|Durban|
|Actual<
premia.<sup>22</sup> We also use firm-level average wage (discounted and averaged over 2010-2015) as a quality indicator. Finally, we also classify firms as foreign-owned if foreign ownership is above 50%. In the Hungarian context foreign ownership is a strong predictor of firm productivity, export orientation, and quality (Kaminski, 1999).

In our main empirical analysis, we restrict the sample to men and use workers aged between 52-57 (with workers aged 52-54 serving as the control group and workers aged 55-57 comprising the treatment group). We do not study the employment change of workers older than age 58 as those workers could retire before the reform, but not after the policy change due to the elimination of some early retirement options.<sup>23</sup> We restrict our sample to the non-retired population to ensure that the estimated employment effects are not driven by the aging-out of already retired individuals from our sample. Appendix Table B5 shows that the estimated employment change and the heterogeneity patterns remain similar if we include retired individuals in the sample. For the workers in our sample, the retirement age was 65 (and 64 for some older cohorts). We find no evidence that the cohorts with slightly older normal retirement age behave differently at age 52-57 so our main estimates are not driven by anticipation effects stemming from extending the retirement age.

Table 1 provides summary statistics on our data. The top panel suggests that the treatment and the control age groups are remarkably similar in terms of employment, wages and share of white collar jobs. The middle panel summarizes the distribution of treatment and control workers across high- and low-productivity firms. For each measure (except for foreign ownership), we divide firms into above-median and below-median groups, taking the median based on all private sector workers, irrespective of their age. The share of workers at high-productivity firms is very similar in the treatment and control groups. Finally, in the bottom panel we examine the industry composition of treatment and control workers
. 6|
|Health Impacts ........................................................................................................ 6|
|3.<br>PUBLICPERCEPTIONS OFCLIMATECHANGE INTAJIKISTAN............................................. 8|
|Survey Module on Attitudes toward Climate Change ............................................ 8|
|Climate Change: Awareness of the Main Causes and Consequences .................... 8|
|Climate Change Impacts of Greatest Public Concern ........................................... 10|
|Public Perceptions of the Seriousness of the Climate Change Problem ............... 12|
|Climate-related Hazards during the Past 10 Years ........................................
; * denotes statistical significance at the 10 percent level, ** denotes statistical significance at the 5 percent level, and *** denotes statistical significance at the 1 percent level.

Third, table 2 further demonstrates that the lack of a correlation in the past month is not being obscured by violence that occurred previously. Specifically, column (4) re‐estimates the baseline specification, but adds lagged conflict events up to five months before the WFP survey. However, again, there is little evidence of a correlation between food coping strategies and any of these variables describing past or future violence, and one cannot reject the hypothesis that all additional lagged violence coefficients are jointly equal to zero (p‐value 0.829).

Fourth, table 2 demonstrates that there does not appear to be any correlation between violence and food coping strategies in any of the years of available data. Specifically, column (5) re‐estimates the baseline specification, but interacts the violence measure with year dummies and includes all lower‐order terms in the specification. The results suggest that one cannot reject the hypothesis that there is no correlation between violence and food coping strategies in 2016, 2017, or 2018.

14
_Schooling Levels.—_ We construct a score for each grade and pool across grades within each schooling level to produce primary and secondary school scores. We distinguish primary from secondary schooling since enrollment rates drop off between levels in many developing countries. This introduces a potential selection term in secondary school scores, with the highest performing students progressing in the system, biasing scores up due to selection rather than actual learning.

Conceptually, the broader categories of ‘primary’ and ‘secondary’ scores enable us to categorize learning at schooling levels across assessments which span multiple grades and age groups. If the test is designed for an age group (for example, PISA) we code it at the relevant schooling level (for example, secondary for PISA). We specify an approach to including specific grade levels to ensure we have a tight grade interval within one to two years to minimize scope for grade-fixed effects. While the interval is relatively small, it still leaves room for grade-fixed effects rather than test-fixed effects when linking tests. For example, linking PIRLS 2001 grade 4 with SACMEQ 2000 grade 6 might capture a grade difference in PIRLS in addition to difficulty. However, to enable greater country coverage, we put up with the need to expand beyond single grade level intervals. Moreover, these differences are often small and since linking functions are applied to all tests being linked, original ranks will be preserved. An analysis of EGRA in Supplement Figure VIII demonstrates sensitivity to grade. We run a regression with and without grade-fixed effects comparing mean scores relative to a country which participates across all three grade levels 2-4. We find small differences, with near complete overlap in the confidence intervals on the grade and non-grade-fixed estimates. This sensitivity analysis increases our confidence that the EGRA data, and other regional assessment data, is robust to data availability by grade. We also include a variable with grade information in the database to make this transparent.

<!-- Start of picture text -->
-100 -50 0 50 100<br
unit of observation is the distict.  EMIS is the Education Management Information System of the Ministry of Education, Ethiopia.  HSDP is the Health Sector Development Plan of the Ministry of Health, Ethiopia.  WCBS is the District government and City Benchmarking Survey, undertaken by the Ministry of Public Service and Human Resource Development and World Bank.  Skewness refers to Pearson's moment coefficient of skewness.  Column 4 repo teacher ratio are not displayed due to gaps in the panel series; test statistics and autoregressive parameters are not shows for the demographic indicators as these are from a single observation from the census of 2007; test statistics and autoregressive parameters are not shows for the agriculture, revenue, or trade indicators as these are from a a two-period panel dataset, with insufficent observations to conduct the tests.  Figures are rounded to two decimal places.
<!-- Start of picture text --> MHHH-FHHH gaps in use of chemical fertilizer vs. GDP per capita<br>aBGD<br>a<br>oD<br>a<br>fa<br>5 APAK<br>=<br>=<br>fat]<br>a= oma]<br>Ea “ALB<br>= ee eer<br>= ee GTM<br>a ee<br>oni=]a — ne a NGA re.<br>a<br>2<br>= i MV NPLa&GHA ABGR<br>a<br>= ANIC<br>a<br>6.5 ri 75 8 8.5 9<br>log of GDP per capita (PPP, constant USS 2005)<br>Source: RIGA data.<br>a a a<br>eS Ss No Na ee ge < O RS <> we<br>awe oe Ss < e oF a Ke sx R So<br>SX ST SK SS SP oC SX S a<br>xv S<br><!-- End of picture text -->
**Figure 7 - Impact of part time work on returns to education**

<!-- Start of picture text --> 20%<br>15%<br>10%<br>5%<br>0%<br>age 25 age 35 age 45 age 25 age 35 age 45 age 25 age 35 age 45<br>-5%<br>Primary Secondary Tertiary<br>-10%<br>-15%<br>Part-time work (Base scenario) No part-time work<br><!-- End of picture text --> Source: Authors’ calculation based on Encuesta de Hogares 1995-2000 _<u>Tuition</u>_ <u>. If adult learners have to pay for primary and secondary education, the return is</u> diminished by three percent points for primary and secondary education (for a 35 year old). This is a reduction of around 67 percent for primary education and 31 percent for secondary education. This scenario can also be interpreted as the impact of private cost of education other than tuition that is not included in the calculations, such as school supplies and transportation. For tertiary education, the base scenario assumes full tuition. In the case of students attending public tertiary institutions, where tuition fee amount to only 20 percent of the full costs, the return to increases by 11 percentage point compared to the base case scenario of full tuition costs. This represents an increase of 68 percent. The robust return equally prevails for an adult student. A 45 year-old would receive a return of more than 20 percent.<sup>11</sup> _<u>Unemployment</u>_ <u>. Unemployment affects the return through two channels: (i) the expected</u> income increases for workers prior to entering education and after the completion of a higher level of education. Therefore, there is a level effect; and (