rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7. Comparison of numbers from administrative statistics and calculated in the CEQ analysis.**

|ad|ministrative data (a), total in the analysis (b|), ratio (b)/(a),|
|---|---|---|
||mln MDL<br>mln MDL|percent|
|Wages|40,159<br>39,040|97|
|Social insurance contributions (employee's part)|2,488<br>2,670|107|
|Medical assistance contributions (employee's part)|1,771<br>1,601|90|
|Social insurance contributions (employer's part)|9,373<br>7,030|75|
|Medical assistance contributions (employer's part)|1,771<br>1,375|78|
|**Total social contributions**|**15,404**<br>**12,677**|**82**|
|Personal income tax|3,649<br>3,234|89|
|Withhold income tax|n/a<br>10|n/a|
|Property tax|194<br>87|45|
|Patent tax|55<br>73|134|
|**All direct taxes excl SSC**|**3,897**<br>**3,404**|**87**|
|Labor pensions|8,577<br>15,459|180|
|Other contributory pensions|2,034<br>2,859|141|
|**Total contributory pensions**|**10,611**<br>**18,318**|**173**|
|Social pensions|1,311<br>345|26|
|Material aid|814<br>1,051|129|
|Unemployment benefits|1,795<br>840|47|
|Scholarships|30<br>15|49|
|Children and maternity benefits|115<br>234|203|
|Other benefits|2,622<br>1,986|76|
|**All direct transfers (excl. pensions)**|**6,686**<br>**4,471**|**67**|
|Value added tax|16,870<br>8,967|53|
|Import tariffs|5,950<br>1,684|28|
|Excise tax|1,068<br>705|66|
|**Total indirect taxes**|**23,888**<br>**11,356**|**48**|
|In-hospital health|3,272<br>2,079|64|
|<br>Out-of-hospital health|3,025<br>1,922|64|
|**Total health**|**6,296**<br>**4,001**|**64**|
|Primary education|2,546<br>1,618|64|
|Secondary education|4,264<br>2,709|64|
|Post-secondary education|710<br>451|64|
|Higher education|803<br>510|64|
|**Total education**|**8,324**<br>**5289**|**64**|

Source: Ministry of Finance, National Office for Social Insurance, authors’ calculations using HBS-2017 data.

Main summary statistics on household and income structure are presented in Table 8. As can be seen, the families with lowest incomes before any interventions are families with children and pensioners, that do not have much of market incomes. These categories as well as rural residents also rely on remittances and incomes from agriculture more than other groups of population.

17
Frank (1998) averages the estimates of Barrett (1990)94 and Attman (1986) to arrive at the amounts of silver production, trade routes and ultimate destinations that<sup>are</sup> shown in Table 1.11.

Table 1.1 1 <!-- Start of picture text --> World Silver Production, Exports, and Reeeipts<br>(values in thousands of tons)<br>Time Period Location and Amount Produced Shipped to Shipped to Asia<br>Europe directly or on-<br>shipped from<br>Europe to Asia<br>16a' century Americas - 17 17<br>Japan - 2 2<br>17th century Americas - 37 27 13<br>Japan - 7 7<br>18th century Americas - 75 54 26<br>1600-1800 Americas - 3 to 10 up to 25 All to Manila<br><!-- End of picture text --> Source: Frank (1998: Map 3.1, 148) One notable feature of the silver trade is that China received a very large share of this metal from all sources and via all routes. According to Frank's estimates, China received nearly all the silver shipped to Asia via Europe and the Pacific in the 1 7th and 18th centuries. Additionally, Japan's 9,000 tons produced in the 16th and 17t centuries went to China. Thus, for over two and a half centuries up to 1800, a total of 48,000 tons of silver from Europe and Japan and another 10,000 tons via Manila, as well as other silver produced in continental Southeast and Central Asia and in China itself is estimated to have ended up in China. He estimates that this total of about 60,000 tons was approximately half the world's production during that time.<sup>95</sup> More recent calculations by Von Glahn (1 996a) result in lower estimates for the amount of silver that went to China. These are shown in Table 1.12.

Table 1.12

|**ChineseImp**|**ortsofSil**<br>**Japan**|**verbyCountry**<br>**(metric tons)**<br>**Philippines**|**ofOrigin,155**<br>**Portuguese**<br>**shipments**<br>**toMacao**|**0-1700**<br>**Total**|
|---|---|---|---|---|
|**1550-1600**|1,280|**584**|**380**|**2,244**|
|**1601-40**|1,968|719|148|**2,835**|
|1641-85|1,586|108|0|1,694|
|1685-1700|**41**|137|0|178|
|**Total1550-1700**|**4,875**|**1,548**|**528**|**6,951**|

Source: Maddison (2001: Table 2-9, 64); **Von** Glahn (1996a: 140 and 232).
# **The Nexus of Energy Supply and Human Health**<sup>**ǂ**</sup>

# **Govinda R. Timilsina**<sup>**¶**</sup>

**Key words** : Energy and health, Ambient air pollution, Household air pollution, Health impacts of energy, Energy supply chain

## **JEL Classification** : I10, Q53

> ǂ The author would like to thank Mike Toman and the participants of the Global Health Economics Summit, Berlin, Germany (July 25-26 2016) for their valuable comments and suggestions. Prayas Timilsina compiled lists of accidents in various activities of energy supply chains presented in the appendix of this paper. The views and interpretations are of author’s and should not be attributed to the World Bank Group.

> ¶ Senior Economist, Development Research Group, World Bank Washington, DC. E-mail: gtimilsina@worldbank.org
count the emigrants abroad, while _de jure_ censuses might capture some recent or temporary emigrants. Thus it is the censuses from destination countries that can be reliably used in migration research.

Universal coverage is the main advantage of a census, and ‘census rounds’ are generally conducted within ten-year periods from the middle of each decade. For example, the 2010 round censuses are conducted between 2005 and 2014. Censuses are expensive; resources and expertise of the national statistical agencies determine the quality of the data and the results. Many statistical agencies just publish cross-tabulations of the main variables of interest as they process the data according to a priority list. Migration data, unfortunately, are among the least crucial for many countries and are published with a significant lag. While census data provide important snapshots of migration over time, they have two key shortcomings. First, they include only basic variables – such as gender, age, place of birth/nationality and maybe education – and thus cannot be used to answer many policy questions. Second, they cannot be used to analyze recent and nuanced trends. However, almost every country conducts a census and the questions are relatively homogeneous.<sup>4</sup> This degree of standardization and geographical breadth means that census data have become the backbone of most of the available global databases on migration stocks, especially in bilateral corridors.<sup>5</sup> A key challenge arises when the census tries to capture undocumented migrants who might be reluctant to respond due to concerns that their data will be used for identification and subsequent

> 4. The United Nations Department of Economic and Social Affairs Statistical Division (UNDESASD) reports that 202 countries have completed a census since 2005, with 26 more countries planning one. 5. Censuses may be used to calculate migration flows by asking questions pertaining to migrants’ residence in previous years and the date of their arrival in the current location.

5
# 5. Findings

In this section, we present the results from the estimation of the regressions presented in Section 3 and using the data described in Section 4. Our preferred earnings measure is hourly total compensation, which includes wage income and any non‐wage payments such as overtime benefits, measured in nominal terms.<sup>5</sup> As a robustness check we use hourly wages instead of hourly total compensation, and our results do not change.<sup>6</sup> An hourly measure is preferred since there are differences in hours worked between men and women. The top and bottom 0.1% of earnings are trimmed to avoid the outsized influence of outliers. We estimate equations (4) and (14) using a linear probability model for ease of interpretation, and the remaining equations using an OLS regression and robust standard errors.

## a) Gender wage gap and occupational sorting

We estimate equation (1) using the data from the Vietnam LFS.<sup>7</sup> The results suggest that women earned consistently less than men<sup>8</sup> on average across the four rounds of the LFS survey that we included in our study. The coefficient on the female dummy ( �� ) is negative and significant in the earnings function in each of the four years. As figure 3 demonstrates, the gap between male and female earnings remains consistent – corresponding in terms of annual earnings to about 3,000,000 Dong, or $130.<sup>9</sup> However, the hourly compensation increased substantially over this period, and as a result, the gap falls in relative terms from close to 10% in 2011 to nearer 5% in 2014. Note that this is smaller than the gender wage gap observed in other countries – for instance, Blau and Kahn (2016) find that the ratio of unadjusted female earnings to male earnings in the US in 2010 was 79% and adjusting for covariates including human capital and occupation‐industry increases this figure to 91%.

> 5 We do not adjust for inflation since the focus of our analysis is on within‐year comparisons of female earnings to male earnings rather than across year comparisons.

> 6 These results are available from the authors upon request.

> 7 Appendix II presents descriptive statistics of the sample.

> 8 Earnings data in the LFS, and typically in other labor force surveys, are confined to respondents who are employees or “wage workers”, and therefore do not include earnings of own‐account, self‐employed or unpaid workers. It is worth noting that in Vietnam, women are less likely to be in the labor force or be wage workers and more likely to be unpaid workers (see Appendix II for more). The gap in earnings that we examine therefore represents only a partial picture of the difference in economic opportunities between the genders in Vietnam. 9 Assuming a 40‐hour work week and 52 working weeks. Conversion to USD based on an exchange rate in 2017 of 22,700 dong = 1 USD.

13
# **1. Introduction**

One of the main roles of the financial sector is allocation of financial resources to their most productive uses. Financial intermediaries play a crucial role in providing funds to the enterprise sector and stimulating its growth. To date, Egypt’s success in penetrating international markets for manufactures has been disappointing as has its low growth in total factor productivity.<sup>2</sup> Government policies distorting the allocation of resources (including the export-harming structure of tariffs and non-tariff barriers to trade as well as inefficiencies and misallocations through the public sector banks) are thought to be primarily responsible for this disappointing record; however, deficient financial infrastructure also points clearly to the need for examining the arrangements for financing the enterprise sector. Facilitating access to finance by the enterprise sector and especially small and medium enterprises should improve productivity and growth in Egypt.

This paper presents an analysis of the finances of Egyptian listed companies evaluating recent trends in growth and profitability and the effects of the economic slowdown of recent years. The data from financial statements (available up to year 2000) reveal the effect of the economic slowing of the past few years, especially in the construction and real estate sectors and especially in smaller companies. Smaller firms appear to be less profitable and enjoy lower growth, likely because of being particularly adversely affected by many of the sources of the high costs of doing business in Egypt. Egyptian firms are not very highly leveraged, on average. While this may reduce their vulnerability, it raises questions about the adequacy of access to credit. Importantly, smaller firms rely much less on bank finance than do larger ones, financing 40 percent less of their assets in this way. It is likely that these differences are even more pronounced for non-listed small and micro enterprises. This confirms the widely held view that there is a need to improve the availability of credit for small enterprises.

> 2 Non-resource based manufacturing exports account for barely 1 percent of GDP. A standard growth decomposition attributes two-thirds of Egypt’s growth 1995–2000 to physical capital accumulation; one-third to human-capital adjusted labor, and essentially nothing to total factor productivity growth (cf. World Bank: _Egypt: Social and Structural Review_ , 2001).

2
# **2.6 Additional Data to Be Collected**

Apart from the core list of indicators, and test score data for students in education, we suggest that data on clinic and school characteristics are collected as part of the survey. These data could be collected at a very low marginal cost and would be useful in case one wants to add additional indicators based on the availability and quality of infrastructure and inputs, and for policy analysis and research using the data.

# **3. DATA SOURCE**

The data used to construct the DSI indicators will be based on facility surveys in health and education. All the necessary data needed to construct DSI indices would be collected through a single survey in each sector carried out at the frontline facility level, complemented by budget data collected at the central level.

The methodology to collect data on each of the indicators is based on international best practice for PETS, QSDS, SAS and observational surveys. The survey data come from direct interviews with school and health facility managers responsible for managing financial and material resources at the frontline and providing services, as well as frontline staff. Records from higher level government are also used to measure overall resource allocation. Such data collections are done independently from governments in order to ensure objectivity.

The data collected will be based on a representative sample of public primary providers. To ensure representativeness with a sample of 200 units, the sample will be stratified along relevant criteria (like urban-rural location).

# **4. CRITERIA FOR SAMPLE UNITS**

Primary care is delivered by different types units. In order to allow for a meaningful comparison across countries, it is important to define “similar” service delivery units in all countries. To this end we will use the following operational criteria: 1) The health unit provides primary health services defined as outpatient consultations, antenatal care, normal deliveries, immunization and family planning.

2) The health unit allocates at least 75% of its resources to primary health care services.

3) The health unit is the main source of primary health care for a significant proportion of the population (at least 25%).
# **provides further confirmation that poverty in India is lower in 2017 than in**

**2011** . Consumption trends in past rounds of the IHDS and NSS surveys have tracked each other closely -- both surveys were conducted in 2004 and 2011 and predicted comparable drops in extreme poverty over this period. A limitation of IHDS-3 is that it is limited to the states of Bihar, Rajasthan and Uttarakhand. For this validation exercise therefore, we restrict the CPHS sample to these three states.

The IHDS captures consumption using the mixed recall period whereas the CPHS consumption used in our analysis corresponds more closely to the uniform recall period. Furthermore, IHDS-3 consumption values reported in Desai (2020) are in constant 2017 values and deflated using the _monthly_ CPI-AL and CPI-IW series. The consumption values in our analysis are in constant 2011 terms deflated using _yearly_ CPI-AL and CPI-IW series. For these reasons, we will be comparing changes in real consumption across the two sources (rather than comparing levels).

Real consumption grew at an annualized rate of 2.7 percentage points between the IHDS 2011-12 and 2017. The average annualized consumption growth over the same period in our analysis (approach 2) is 1.5 percent.<sup>28</sup> Real consumption growth in the IHDS-3’s rural and urban samples are 3.8 and -0.7 percent per year. By comparison, consumption growth in rural and urban in our analysis is 1.7 percent and 0.6 percent, respectively. Both surveys therefore point to faster growth in rural areas than urban areas. The differences in consumption recall and deflators used in the two surveys could account for the difference in magnitudes of the observed growth rates.

Correlates of consumption, such as durable asset ownership, are similar across the two surveys. Thirty-two percent of households in the IHDS-3 states own motorcycles and cars and 21 percent possess air coolers and air conditioners. In the reweighted CPHS, ownership shares of these two assets are 34 and 22 percent, respectively. Growth in monetary and non-monetary indicators in the IHDS-3 therefore are consistent with the observation that poverty in 2017 is lower than in 2011.

**Another assessment of poverty since 2011 can be made by comparing rural headcounts to rural wages produced by India’s Labor Bureau.** Monthly wages for agricultural and non-agricultural occupations are available since 1998. We take a weighted average of wages across occupations to construct a composite monthly rural wages series. The series is then deflated using monthly CPI-AL series and collapsed at the yearly level by taking a simple average across months.

Figure 22 correlates the growth in average annual wages for rural workers with yearon-year changes in rural poverty headcounts from our analysis (approach 2). As real

> 28The average real consumption in NSS-2011 for the three states is 1259.01 (constant 2011 rupees). For rural and urban areas, the mean consumption in NSS-2011 is 1141.57 and 1885.60 respectively.

50
While the Pass Laws Act had already been repealed in 1986 and although identification would be cleaner if data from before 1986 were available, this timeline does not pose a major threat to our identification strategy. This is because the Group Areas Act and the Population Registration Act were still in place, and as such the black population was still severely constrained in its choice of residence until June 1991.<sup>10</sup>

#

Distance to the nearest homeland is used as an instrument for migration flows in order to causally identify the effect of migration on population distribution. Figure 4 shows that the relationship between this distance and population growth between 1991 and 2011 is strongly negative, both at short and longer distances. In this figure we pool neighboring observations into discrete bins to improve clarity. We specify 100 bins in total, which puts around 20 observations into each bin. The log linear specification seems a good fit for the data. The validity of the instrument relies on the conditional quasi-random allocation of homelands, which has been argued for in Section 2. The assumption may be violated for areas adjacent to the homelands however, as they are likely to be affected by economic spillovers from the neighboring homeland in a variety of ways that are not related to the cost of out-migration from the homelands. To adjust for this problem, we exclude areas within 10 km from the homelands as a robustness

> 10The data from 1991 does not cover the entirety of South Africa. One general drawback of the dataset is that it does not cover the homelands. This does not affect the analysis since we only look at areas outside the homelands. Another more relevant drawback is that there are a few areas that are not covered within South Africa (see Figure 7 in appendix B). This is due to two reasons. First, Statistics South Africa only has a partial map of the census enumeration areas in 1991. Therefore, part of the census data cannot be geographically referenced. Secondly, due to violent turmoil at the time, some areas could not be visited by enumerators and no data are available on a granular level. This is potentially beneficial for our analysis since we exclude areas with high racial tension, which could otherwise bias our results. So, while this reduces the number of observations and therefore the statistical power in the empirical analysis, our parameter estimates will remain consistent.

18
**Appendix A2 Asset Beta Estimates**

- Key: e indicates an equity rather than an asset beta estimate.

- 'indicates that security has severe infrequent trading problems and is likely to be biased downwards.

A2.1 Europe

# A2.1.1 **Austria**

Index: Austria traded index (ATX)

||OMV AG**(elec)**|**Vienna Airport**|
|---|---|---|
|Five-year beta|0.85|0.50 (1992-95)|
|1990|1.34||
|1991|0.65||
|1992|0.83|-|
|1993|0.69|0.71|
|1994|0.75|_1.05_|

# A2.1.2 **Belgium** Index: Brussels Stock Exchange General Index

||**Electrabel (electricity)**|**Tractebel (electricity & gas)**|
|---|---|---|
|Five-year beta|0.40|0.46|
|1990|0.26|0.97|
|1991|0.33|0.33|
|1992|0.41|0.34|
|1993|0.48|0.28|
|1994|0.64|0.39|

45
time, and more likely to have higher years of completed schooling as they begin to enter school. As in Gertler and Fernald, program impacts are generally apparent in the comparisons between the treatment and matched comparison groups, and not in the comparisons between the two treatment groups that were randomly assigned to different amounts of program exposure.

Three recent papers analyze the effects of daycare or preschool attendance in Bolivia and Argentina. Behrman, Cheng, and Todd (2004) use non-experimental data to evaluate the impact of a Bolivian pre-school program, the _Proyecto Integral de Desarrollo Infantil_ , or PIDI. The outcome measures include “a battery of tests of bulk motor skills, fine motor skills, language and auditory skills, and psychosocial skills”. (The authors do not provide details on the exact nature of these tests.) The PIDI program provides full-time daycare, nutritional and educational services to children between the ages of 6 and 72 months in low-income areas. Behrman, Cheng, and Todd use propensity score matching to estimate program impacts. First, they compare program participants with a matched comparison group of non-participants—they refer to these as average treatment impacts. Second, they compare participants with different amounts of exposure to the program—they refer to these as marginal treatment impacts. The identifying assumption for the estimates of average treatment impacts is that there is no selection into the program on the basis of unobservable characteristics of households or children. This restriction is loosened somewhat in the estimates of marginal treatment impacts although, conditional on program participation, unobservables are still not allowed to determine the duration of exposure.

Behrman, Cheng, and Todd (2004) find that, in the comparison of treated and untreated children, there is some evidence of positive program impacts on bulk and fine motor skills, psychosocial skills, and language acquisition. These impacts are concentrated among children ages 37 months and older—for the younger children, ages 6-36 months, the estimated program effects are generally insignificant, and are as likely to be positive as negative. When the results are disaggregated by the length of exposure, effects are most clearly observed among children who have been exposed to the PIDI program for more than a year. Behrman, Cheng, and Todd also calculate cost-benefit ratios for the PIDI program. This is an important concern: At the time of the evaluation, the cost of the PIDI program was approximately $43 per month, in a country with per capita annual GDP of $800 in exchange-rate converted pesos, and $2,540 in PPP terms. The PIDI program is estimated to have an impact on height, cognitive development, and schooling of participating children. Behrman, Cheng, and Todd heroically combine these estimated program impacts with data on wages from Bolivia and a number of other countries to argue that there are positive cost-benefit ratios to PIDI under a variety of plausible assumptions and discount rates.

17