rafmacalaba/datause-encoder-data huggingface dataset:
* 45 -

# 1. T **in Natonal FInnCIDng for** Maternal Health

Global monitoring of maternal care financing is<sup>not likely to emerge without a specific</sup> agreement by the various health ministries.<sup>The problem is not unrelated to the difficulties of</sup> monitoring general health sector expenditures.<sup>Only 20 developing countries report to the</sup> United Nations system on national accounts.<sup>WHO has attempted to encourage function,l</sup> accounting following the Mach and Abel-Smith<sup>(1983)'Manualon Financial Planning for</sup> Developing Countries.' But acceptance of this<sup>procedure has been very slow and, in effect, has</sup> inhibited the ability of governments to document<sup>their progress in the health sector.</sup> Short of improving the entire health statistical system<sup>in developing countries, there will</sup> need to be an agreed effort among Ministries of<sup>Health and their resident external cooperation</sup> advisers to gather maternal health information according<sup>toagreedcriteria. Given the program</sup> overload imposed on health ministry personnel<sup>and their WHO colleagues, how is the work to</sup> be done? The issue merits the attentior. of<sup>the Meeting of Interested Parties. Is this an area</sup> that would benefit from joint financing and technical<sup>cooperation, beginning with a limited</sup> number of developing countries and expanding<sup>over a period of several years? The process of</sup> attracting financing suggests that data collection<sup>may be one of the prerequisites for effective</sup> financial mobilization in that it documents drmand<sup>and justifies the need for additional</sup> financing. For purposes of program justification,<sup>gross estimates are less persuasive. For</sup> example, 72 percent of 40 least developed countries<sup>responding to a WHO survey spend less</sup> than 5 percent of GNP on health (WHO EB/83/2).<sup>Half of another 88'otherdeveloping</sup> countries also spend less than S percent. The<sup>high number of countries that did not respond to</sup> the survey (50 percent) suggest that average<sup>GNP health percentage islower-- possibly 2 to 4</sup> percent. The 1987 DAC chairman's Annual Report<sup>provides a global GNP estimate of</sup> S2,S85,260 million. At the 3 percent level, public sector<sup>health expenditures would approximate</sup> S77.4 billion. Assuming a 1:3 ratio of public<sup>toprivate expenditures, health expenditures by</sup> developing countries may reach S221 billion.<sup>If a tenth of this expenditure is attributed to</sup> maternal health, the global total would approximate<sup>S22 billion.</sup> This type of global estimate is meaningless for<sup>practical purposes. How much maternal health</sup> does a given unit of currency purchase? To<sup>make reasonable progress in this direction, it is</sup> critical to encourage the practice of functional accounting.
savings. Authorizing life policies denominated in US dollars could stimulate the development of the market as would the offer of better targeted fiscal incentives, the growth of private pension plans and the hiring and training of sales staff.

**Table 17: Performance of Life Insurance, 1997-2002**

||1997|1998|1999|2000|2001|2002|
|---|---|---|---|---|---|---|
|Gross Premium Income (mn JD)|20.27|14.57|15.31|16.28|17.40|18.47|
|Growth Rate (%)||-28.1|5.0|6.4|6.9|6.2|
|Reinsurance Ratio (%)|24.4|22.6|21.8|21.0|24.0|23.6|
|Net Claims/Net Retained Premiums (%)|49.8|35.2|39.6|43.7|50.3|42.2|
|Net Commissions/NRP (%)|4.7|10.3|10.5|11.3|10.9|11.8|
|Expenses/NRP (%)|15.7|16.0|15.5|18.2|17.3|18.1|
|Investment and Other Income/NRP (%)|7.5|8.1|6.0|8.2|9.2|7.1|
|Annual Surplus/NRP (%)|37.3|46.6|40.3|35.0|30.8|35.0|
|Addition to Life Fund/NRP (%)|26.1|28.4|29.3|23.2|22.8|21.9|
|Profit Before Tax/NRP (%)|11.2|18.2|11.1|11.8|8.0|13.1|
|Profit Before Tax/Annual Surplus (%)|30.1|39.1|27.4|33.6|25.8|37.4|

Source: Insurance Commission

# **I. Insurance Solvency**

The financial standing and results of individual insurance companies reflect the lines in which they specialize. Those that have a high proportion of fire, marine and general accident business perform well, while those that rely preponderantly on motor or medical insurance suffer from unsatisfactory results. Several companies have strong capital, adequate reserves, appropriate reinsurance arrangements, and low retention ratios of the large industrial risks. They appear to operate efficiently with low costs and short delays in settling claims.

However, several other companies appear to operate with weak financial ratios and exhibit a relatively high occurrence of disputes with policyholders and beneficiaries. They tend to suffer from long delays in settling claims, often forcing their customers to seek recourse to the courts. This clearly affects small and poorer consumers (and third party victims).

The one company that was put under liquidation in 2001 was found to suffer from a large deficiency in reserves. Of the companies that are currently licensed, 3 have unusually low reserve levels (less than 50 percent of NEP for both unearned premiums and outstanding claims) and another 6 probably have inadequate reserves (less than 70 percent of NEP). These companies, which tend to specialize in motor and/or medical insurance, need to undergo special audits, even if they satisfy the required solvency margin, since their reported capital is probably overstated.

19
this is the first econometric study of the activities of agents of a microfinance institution in a developing country, certainly the first in a country as poor and financially underdeveloped as the DRC. Conducting such a study in a country that most often lacks reliable data and where local research capacity is limited was challenging, but this also marks our study as an early contribution to better understanding the economy in DRC.<sup>11</sup> Our work is closely related to a recent literature that explores how lowering the transaction costs of transferring funds within social networks improves household financial management. Jack and Suri (2014) have shown how mobile telephony has reduced the costs of such within-network transfers in Kenya and find that mobile money has helped households to smooth consumption in the face of economic shocks. Specifically, they find that shocks reduce the consumption of nonusers of a widely adopted mobile money service called M-PESA by 7 percent, while shocks have no significant effect on the consumption of households with an M-PESA user. Similarly, Yang and Choi (2007) find that shocks to the incomes of Philippine households are associated with significant increases in the international remittances that they receive, an indication that those remittances could be used to smooth consumption.

Both the volume and diversity of remittance senders increase after a shock and the average distance from senders to receivers of remittances also increases substantially (Jack and Suri, 2014), suggesting that M-PESA has enabled households to expand or make fuller use of their social networks at lower cost. Relatedly, Aycinena, Martinez, and Yang (2009) show that reductions in remittance fees increase the frequency with which Salvadoran migrants receive them. Jack and Suri (2013) also find that the purposes of remittances differ between users and non-users of M-

> 11 There are, however, less formal studies that describe the business model and provide information on the financial viability of agents. See, for example, http://www.cgap.org/data/india-banking-agents-survey-2012 and <u>http://www.helix-institute.com/data-and-insights/agent-network-accelerator-survey-uganda-country-report-2015</u> for evidence from India and Uganda, respectively.

5
**Table 8. Effects of COVID-19 on decline of production, sales, investment, employment, and general performance**

||Prod<br>|uction<br>|Sa<br>|les<br>|Invest<br>|ment<br>|Empl<br>|oyment<br>|Perfo<br>|rmance<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||All|Existing|All|Existing|All|Existing|All|Existing|All|Existing|
|Cash|-0.123*|-0.081|-0.109|-0.064|-0.280***|-0.268***|0.035|0.060|-0.196*|-0.181*|
||(0.067)|(0.060)|(0.094)|(0.087)|(0.091)|(0.075)|(0.246)|(0.267)|(0.107)|(0.092)|
|Matching|0.075|0.103|0.081|0.087|-0.024|0.019|0.093|0.063|-0.035|-0.011|
||(0.074)|(0.077)|(0.094)|(0.096)|(0.115)|(0.118)|(0.196)|(0.166)|(0.105)|(0.106)|
|Control group|0.508|0.524|0.578|0.606|0.219|0.230|0.024|0.024|0.243|0.255|
|mean|||||||||||
|Standard|0.501|0.500|0.494|0.489|0.414|0.422|0.154|0.154|0.430|0.436|
|deviation|||||||||||
|S.E. clustered|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|Municipality|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Block fixed-effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Pseudo R2|0.068|0.058|0.080|0.054|0.056|0.044|0.121|0.132|0.053|0.046|
|N (Post)|1,107|970|1,107|970|1,107|970|1,107|970|1,107|970|

_Note:_ ***p < .01, **p < .05, *p < .1. Each of the dependent variables, production, sales, investment, employment, and general performance is a dummy that equals one if it decreased for the firm due to COVID-19. The list of controls includes a dummy that equals one if the firm is already existing (vs new); dummies for whether the firm sector is industry or services, with agriculture being the reference; a share of correct recalls by the owner from a digit span memory test; a dummy that equals one if the owner is female; the age and number of years of professional experience of the owner; dummies for whether the owner is Christian or Muslim; dummies for whether the owner is of the two largest ethnic groups (Bissa or Mossi); dummies for whether the owner reads and writes French and a local language or reads and writes a local language only; dummies for whether the owner attained primary, secondary, or other education levels, with the excluded being no education level; the size of the owner household; the number of assets owned; and dummies for whether the owner is a household head and whether a member in the owner household is a community leader. The control group mean refers to the follow-up survey. The detailed regression results are provided in Table A.11 in the appendix.

30
well as a less extreme scenario where the skil ~~l-~~ biased technical change offsets only half of the substitution effect (i-e., « = 1/209). In the former case, technical change fully reverses the substitution effect in Eq. (15). In the latter case where k = 1/205, K—-l/og 1/208 Eq. , ~~WH~~ _ ~~aH~~ — ~~gH~~ (15) becomes ~~ut~~ =To ~~(2)~~ =Io ~~(4)~~ .

Table 3 below summarizes values of the parameters we use in the main simulation. Wage proxies. ~~—~~ We use the wage of a low ~~-s~~ killed young native worker (i.e., wry) as a reference wage, and use proxies for the skill, age and immigrant premia to compute the wage rates of other workers. We use skill premium data from Hendricks (2004) for each country. Average immigrant wage premium for each country is taken from DOP, where they use figures from a number of papers published between 1993 and 2005. We compute age premium ourselves from the American Community Survey data and use this for all countries in our sample. The following are used to calculate the average wages of workers in the other seven groups:

- e wron = (US age premium) xwryn

- e wayn = (skill premium) xwzyn

- e won = (US age premium)x (skill premium)x wry

- e wryr = (immigrant premium) xwzyy

- e wror = (immigrant premium) x(US age premium) xwyy yn e wayr = (immigrant premium)x (skill premium)x wry wn e wor = (immigrant premium) x(US age premium)x (skill premium) x wry7

Productivity and preference parameters. When employment levels (qsaj) and wage rates (ws) are determined, we sequentially use Eqs. (13), (14) and (15) to calibrate the relative productivity parameters. First, we insert wage and employment data into Eq. (13) and calibrate (Cyyv,lHom,l tym, tom). This allows computing the composite qsa using Eq. (6). Second, we compute average wage for cach SA group as ws, = ~~SANA SAN TESARISAR~~ and substitute these wage rates into Eq. (14) to calibrate (Ty4,Tra). This allows computing gs using Eq. (5). Third, we compute the average wage rate by skill group as wg = ~~Ss~~ <u>dsO</u> and plug them into Eq. (15) to calibrate ['y. This allows computing q using Eq. (4), and calibrate the TFP to match the GDP data using Eq. (3). We know that a fraction 1 — a of GDP serves to remunerate labor. Using 1 — a = 2/3, we calibrate the level of our reference wage, wry yn, to match the national wage bill.

As for preference parameters, we calibrate ds, as a residual from Eq. (11). This gives sam = Weiv (dsam/Qsam). In the extension with involuntary unemployment, we use 18
32

|**COUNTRY**|PROJECT|LEGAL COMPONENT|FINANCIERS|
|---|---|---|---|
|Angola (Cont.)|Financial Institutions Modernization<br>Project (Cr No. 2421 ANG),<br>September 15, 1992|Improvement of financial legal and regulatory framework,<br>including the following activities: (a) application of banking<br>legislation through design, publication and distribution of a<br>banking operational manual; (b) compilation, harmonization,<br>publication and dissemination of the banking legal and<br>regulatory framework; (c) strengthening of legal and technical<br>capabilities of banking institutions' staff; (d) strengthening of<br>the National Directorate of Registries and Notarial Services;<br>and (e) review and development of modem banking<br>regulations.|IDA $21 m.|
|Argentina|Second Tax Administration<br>(Loan No. 3460 AR), September 19,<br>1992|Legal technical assistance for the review and codification of<br>existing tax laws and regulations in order to produce<br>simplified and coherent legislative publications and develop a<br>tax legislation database.|IBRD $20 m.|
||Second Public Enterprise Reform<br>Adjustment Loan<br>Japanese Grant Agreement, June 21,<br>1993|Legal technical assistance in connection with: (a) the<br>preparation of different components of the privatization<br>program of Argentina's public enterprises; and (b) the<br>preparation of all agreements, charts and regulations required<br>for the functioning of the backstop facility to be financed by a<br>proposed $500,000,000 loan for the Capital Market<br>Development Project.|Japanese Grant 1168.8 m.|
Policy Research Working Paper 8268

# **Abstract**

This paper finds that capital and labor incomes in the United States have become more closely associated since the 1980s. This contributed to the well-known increase in the top 1 percent’s share of total income, exacerbating rising inequality in capital incomes and earnings. The paper shows that the trend in the association is U-shaped, as the recent increase contrasts with a tendency toward a weakening association until the 1980s. The paper uses data derived from tax records, studies the asymmetries in the association, tests for robustness to alternative income definitions, and discusses the potential role of declining top marginal tax rates.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at clakner@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
and Lee, 2000). The superior human capital of China can be seen as well in infant mortality data, which are a good summary indicator of health status. In 1990 China’s infant mortality rate was 38 per 1000, far below India’s 80 or RODA’s 69 (Table 2). Despite its good human capital, in 1990 China had about the same per capita income as Bangladesh, India, Pakistan, and Vietnam, and was substantially poorer than Indonesia, Philippines, and Thailand. It is hard to get reliable data on wages, but the available data suggest that China had wages somewhat lower than those in Bangladesh, India, and Pakistan, and far behind those in the more advanced Asian developing countries (Table 3).

The Chinese refer to their reform program as “ _Gai ge kai fang,”_ which translates as “change the system, open the door.”<sup>1</sup> The whole reform program is often referred to in brief as the “open door policy.” This highlights that a key component of Chinese reform has been trade liberalization and opening up to direct foreign investment, but not opening the capital account more generally to portfolio flows. By 1990 China’s economy was far more open than those of the other low-wage countries in Asia: China’s average import tariff was 40%, well below those of Bangladesh (94%), India (82%), or Pakistan (65%). Thailand (40%) had the same average tariff rate in 1990; the Philippines (28%) and Indonesia (21%) were more open still, but with significantly higher wages they were not competing directly with China (Table 3). After joining the WTO China’s average tariffs have dropped below 10%, and to around 5% for manufactured imports.<sup>2</sup> It initially welcomed foreign investment into “special economic zones,” but it is important to note that some of these were very large, amounting to urban areas of 20 million people or more. The positive impact of foreign investment in these locations led to a more general 8
_Below cutoff_ also fulfills the second important assumption of IV analyses (Murnane and Willett 2010). Namely, the below cutoff variable is strongly correlated with Vij in the first stage of the IV regression (see appendix table S2 and appendix table S2 in the supplemental appendix, available at http://wber.oxfordjournals.org/). Specifically, the first stage results show that the instrument has a strong and statistically significant (at the 1% level) relationship with the endogenous regressor. The weak identification tests (using the Craig-Donald Wald F Statistic) all reject the null hypotheses that the equations are weakly identified (with a p-value < 0.01).

# _2.3.3 Coarsened Exact Matching (CEM) Analyses_

As a robustness check on our IV analyses and also to see whether our IV analyses hold over a broader range of data (i.e., because the IV analyses were only for students from twentyone counties with HSEE cutoff data), our third analysis is a matching exercise. This third analysis isolates the sample of vocational and academic high school students that are similar on baseline characteristics by using coarsened exact matching or CEM. The CEM procedure is comprised of three steps. In step one, each variable is recoded (or “coarsened”) so that substantively similar values of the variable are grouped and assigned the same numerical value.

In step two, students are matched “exactly” on the coarsened data: if either a vocational high school student or an academic high school student does not find one or more matches on the coarsened data, that student is dropped from the sample. In step three, the data are “uncoarsened” to enter academic high school. Nevertheless, this is the exception and not the rule. As such, this phenomenon should not substantively change our analyses.
# **IV. Data and methods**

# _Data_

We rely on four sources of data collected as part of the _Pantawid_ evaluation. Our primary data source is a follow-up household survey conducted in October and November of 2011 – two-and-ahalf years after the start of the intervention and during the middle of the 2011/2012 school year. In each of the 130 villages in the evaluation, survey data were collected from a random sample of both eligible and ineligible households in treatment and control communities.<sup>10</sup> This survey covered a range of topics including school attendance by children aged 6 to 17, and work by children aged 10 to 17. Questions on children's school participation were addressed to the child’s mother, guardian, or main caregiver, while the questions on work were addressed to the child herself. Children were asked not only about current work but also, albeit in less detail, about work prior to the start of the program for the calendar years 2007, 2008, and 2009. We use these recall data to explore baseline balance in child work as the baseline does not contain this information. Appendix 2 explains how we construct our outcome variables based on this data.

The second source of data is the baseline assessment of household-level demographic and socio-economic measures used to construct the PMT score. We use these data to assess balance of key baseline characteristics across treatment and control communities at baseline. Our third data source is a survey administered to village leaders concurrently with the household survey, which includes an assessment of the average daily wage of a male laborer in the village as well as measures of community access to services, such as the distance from the village hall to the nearest public 10 In each village, the survey was administered to 10 poor households (those with a PMT score below the eligibility threshold) with children aged 0 to 14 and/or a pregnant woman, 10 non-poor households with eligible children and/or pregnant women, 5 poor households without eligible children or pregnant women, and 5 non-poor households without eligible children or pregnant women.

10