rafmacalaba/datause-encoder-data huggingface dataset:
13 and it greatly increases the prospects for survival and development of her living children.

# IV. COST-EFFECTIVENESS OF FAMILY PLANNING SERVICES IN PREVENTING MATERNAL MORTALITY AND MORBIDITY

Family planning services appear to compare favorably from the standpoint of cost-effectiveness, with other means of preventing mortality and ill health in women in their childbearing years. To our knowledge, the cost-effectiveness of family planning services in imoroving maternal health has not been measured directly from actual country program data. In the absence of such empirical data, estimates depend on assumptions regarding fertility, contraceptive use, and the cost of delivering health and planning services in a given setting. A recent study attempted such estimates.

Herz and Measham (1987) estimated the cost of averting maternal deaths by taking two hypothetical populations drawing data from Asia and subSaharan Africa and calculated the cost of maternal health and family planning programs from estimates of their constituent parts (personnel, equipment, supplies, etcetera), again using composite data from several countries. The results are summarized in tables 3-6. Table 3 sets forth the demographic, economic and health infrastructure characteristics of two hypothetical districts in which 'Safe Motherhood' programs are to be mounted. Tables 4 and 5 give the costs of the two illustrative programs, one a 'moderate" program effort, the other a more limited effort. And table 6 provides the estimated cost and impact of the two illustrative programs.

The Herz/Measham estAme-tes are for the incremental capital and recurrent costs of a maternal health and family planning program in the hypothesized settings. The cost per maternal death averted is US$4,800 and US$6,250, respectively, for the illustrative programs. The more limited effort appears more cost-effective mainly because it is usually easier and less costly to reduce mortality from a very high level to a slightly lower level, than to achieve equal reductions at lower levels of mortality. We assume, conservatively, that the cost of averting one maternal death through family planning services is no more (or less) than the cost of averting a maternal death through the non-family planning elements of the "Safe Motherhood" program. This assumption is supported by results of four illustrations of the model set out in Annex 1 of the referenced paper (Herz and Measham, 1987). The main point is that, while no hard empirical evidence is available, much program experience suggests that family planning is one of, if not the, most cost-effective means of averting maternal deaths.

Another crude way of estimating the cost of averting maternal deaths by family planning services is applying the standard couple-year of protection concept regarding contraception (Gorosh and Wolfers, 1979). It costs on average, US$20 per year to provide contraceptive protection to a woman (World Bank, 1984). If each year of protection prevents 0.4 births, on average, and if the maternal mortality rate (ratio) is 800 per 100,000 live
Bennedsen and Feldman (2002) extend these models of informational lobbying with a single decision maker, to address the fact that ultimately the purpose of lobbying is to influence legislation, which is made by Congress. Hence, any complete theory of informational lobbying must take into account the process by which majorities are formed during legislation of policy. They develop a theory whose main conclusion is that the ability to _create_ majorities in Congress provides the necessary incentives to lobby groups to carry out their activities.

The findings in Wright (1990) empirically motivate the relevance of the BennedsenFeldman model. Wright conducted a survey of lobbies that had contacted Ways and Means committee members and Agriculture committee members of the U.S. House of Representatives. His inference that money contributions _plus_ informational lobbying influenced voting in Ways and Means affirms the first view that informational lobbying does matter. Further, Wright found that lobbying mattered more than money to Ways and Means committee members (that is, money bought access in order to informationally lobby). Wright’s findings about lobbying of Ways and Means member have two implications: (i) taking note of the fact that decisions are made not individually but in committees of the Congress, rather than target one individual lobbyists allocate their scarce resources over a set of influential decision makers, and (ii) lobbying is informational. The Bennedsen-Feldman contribution is thus more relevant than previous theoretical models that presumed informational lobbying of an individual decision maker.

Notably, Wright finds very weak evidence of informational lobbying of the House Agriculture committee, the most influential policymaking committee in agriculture. The specific content of the five-yearly Farm bills are determined in this committee. In Wright’s data strong collinearity among regressors precluded reliable inferences about informational lobbying of the Agriculture committee. Wright attributes the weak finding to the fact that the Agriculture committee deals with a narrow and well defined set of issues on a periodic basis (as different from Ways and Means which attacks a range of diverse issues, often on an ad hoc basis). Both, leaders and rank and file members of the Agriculture committee, have ample opportunity to regularly interact with lobbies. The preferences of these lobbies have been fairly constant over time, and so those preferences are well known to Agriculture committee members (as different from Ways and Means whose members encounter a variety of 10
Isik, I., Gunduz, L., and Omran, M., 2004. “Managerial and Scale Efficiency in the MENA Banking: A Panel Study of the Jordanian Banking Sector”.

Khwaja, A., and Mian A., 2005. “Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market”. _The Quarterly Journal of Economics_ , 120(4), 1371-1411.

Kobeissi, N., and Sun, X., 2010. “Ownership Structure and Bank Performance: Evidence from the Middle East and North Africa Region” _Comparative Economic Studies_ 52 (3), 287-323.

Körner, T., and Schnabel, I., 2010. “Public Ownership of Banks and Economic Growth - <u>The</u> Role of Heterogeneity” Working Paper Series of the Max Planck Institute for Research on Collective Goods 2010_41.

La Porta, R., López-de-Silanes, F., Shleifer, A., 2002. “Government Ownership of Banks” _Journal of Finance_ 57 (2), 265-301.

Levy-Yeiati, E., Micco, A., and Panizza, U., 2007. “A Reappraisal of State-Owned Banks” _Economica_ 7(2), 209-247.

Madeddu, O., 2010. “The Status of Information Sharing and Credit Reporting Infrastructure in the Middle East and North Africa Region” unpublished manuscript (available at MENA Finance Flagship website: http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/MENAEXT/EXTMNAREGTOPPOVRED/0,, contentMDK:22734614~pagePK:34004173~piPK:34003707~theSitePK:497110,00.html) Micco, A., and Panizza, U., 2007. “Bank Ownership and Lending Behavior” _Economics Letters_ 93, 248-254.

Micco, A., Panizza, U., and Yañez, M., 2007. “Bank Ownership and Performance: Does Politics Matter?” _Journal of Banking and Finance_ 31 (1), 219–41.

Micco, A., Panizza, U., and Yañez, M., 2004. “Bank Ownership and Performance” InterAmerican Development Bank, Research Department Working Paper 518 OECD (2009). Policy Brief on Improving Corporate Governance of Banks in the Middle East and North Africa.

Olson, D., and Zoubi, T., 2010. “Efficiency and Bank Profitability in MENA Countries”.

Omran, M., 2007. “Privatization, State Ownership, and Bank Performance in Egypt” _World Development_ 35 (4), 714–733.

Rocha, R., Farazi, S., Khouri, R., and Pearce, D., 2010. “The Status of Bank Lending to SMEs in the Middle East and North Africa Region: The Results of a Joint Survey of the Union of Arab Banks and the World Bank” unpublished manuscript (available at MENA Finance Flagship website: http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/MENAEXT/EXTMNAREGTOPPOVRED/0,, contentMDK:22734614~pagePK:34004173~piPK:34003707~theSitePK:497110,00.html) Rudolph, H., 2009. “State Financial Institutions Mandates, Governance and Beyond” Policy Research Working Paper 5141. The World Bank, Washington DC.

Scott, D., 2007. “Strengthening the Governance and Performance of State-Owned Financial Institutions” Policy Research Working Paper 4321, The World Bank, Washington, DC.

29
GDP ratio by up to 10 percentage points in year 10 relative to baseline. Fiscal consolidation reduces output in the years following reconstruction with the effect that debt is only 3.4 percentage points above baseline 30 years after the disaster. Capital stock and potential and actual output are, however, close to their baseline.

Imposing temporary budget-neutral cuts in public expenditure to finance reconstruction investment (reported in table 5), reduces aggregate demand beyond the disaster’s negative output shock and causes deflation (relative to baseline). Lowering government consumption by about 45 percent for four and a half years imposes significant short-run costs via lower demand and higher unemployment in order to achieve a quicker recovery relative to the debt-financed reconstruction scenario with most macroeconomic variables close to their no-disaster baseline values 10 years after the disaster shock. In the long run, the scenarios are alike with most variables close to baseline in year 30. Interestingly, mobilization of additional government revenue to finance reconstruction (reported in table 6) has similar effects on output as debt-financing without the cost of a larger debt burden and the need for fiscal consolidation in future. The tax-and-spend scenario allows the positive effects of fiscal stimulus to compensate for the disaster-induced fall in aggregate demand, while stoking slightly more consumer price inflation than under debt-financing.<sup>8</sup>

# 4.2. Threshold and cascading effects and fiscal policy

We double the destruction of capital caused by a natural disaster to 10 percent to test for the presence of threshold effects in MFMod. We find that a doubling of the destruction of capital translates into roughly a doubling of its effects on the economy. The effects are, however, disproportionately larger when the economy cannot scale its reconstruction efforts to the shock size. As in section 3, we find that sound fiscal policy can strengthen economic resilience by providing the fiscal space necessary to fund reconstruction at the necessary scale.

In the baseline reconstruction case, wherein half of public investment is directed towards reconstruction, the time to repair all affected capital stock increases from five to ten years. Keeping a larger damaged stock for longer affects the economy negatively. The depression of aggregate demand and all its components persists in the medium run. For example, output 5 years after the disaster is more than 2 percentage points smaller with a large shock (-3.9 percent vs. -1.7 percent,

> 8 Countries with low tax revenue to GDP may benefit from raising tax revenue above a certain threshold (see, for example, Gaspar, Jaramillo, and Wingender 2016). The nonlinear effect of raising tax on output is not modeled in the standard version of MFMod used in this paper.

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# Table A1. Surveys included in the analysis

|||||**Instru**|**ment**|**Reference**|||
|---|---|---|---|---|---|---|---|---|
|**Country**|**Year**|**Coverage**|**Survey type**|**Stylized**|**Diary**|**population**|**Sample size**|**Reference period**|
|Albania|2010-2011|National|Time use||✓|10+|2,250 HH|Daily|
|Argentina|2013|National - urban|Module in<br>HH survey|✓||18+|65,352 individuals|Daily|
|Colombia|2012|National, 5 urban regions,<br>and 4 rural regions|Time use|✓||10+|44,236 HH; 151,099<br>indiv|Daily|
|Ecuador|2012|National, urban and rural|Time use|✓||12+|22,968 HH|Weekly|
|Ghana|2009|National|Time use||✓|10+|4,193 HH (9,297<br>individuals)|Daily (1 hour)|
|Guatemala|2011|National|Module in<br>HH survey|✓||7+|14,337 HH|Daily|
|Iraq|2012|National|Module in<br>HH survey|✓||10+|25,146 HH|Previous week|
|Mexico|2014|National|Time use|✓||12+|17,000 HH|Weekly|
|Moldova|2011-2012|National|Time use||✓|10+|15,600 HH|Daily (10 mins)|
|Mongolia|2011|National|Time Use||✓|12+|4,000 HH|Previous week|
|Pakistan|2007|National|Time Use||✓|10+|19,600 HH|Daily (30 mins)|
|Peru|2010|National, urban and rural|Time use|✓||12+|4,580 HH|Weekly|
|South Africa|2010|National|Time use||✓|10+|14,553 indiv|Daily (30 mins)|
|Timor-Leste|2007|National|HH survey|✓||8+|8,355 individuals|Weekly|
|Turkey|2006|National, urban and rural|Time Use||✓|15+|5,070 HH (11,815<br>indiv)|Daily|
|United States|2013|National|Time use||✓|15+|26,400 HH|Daily (time reported bydiarist)|
|Uruguay|2013|Urban|Module in<br>HH survey|✓||14+|3,391 HH (7,447<br>individuals)|Daily|
|West Bank<br>and Gaza|2012-2013|Formal and informal hh,<br>rural/urban, refugees|Time use||✓|10+|5,903 HH|Daily, 30 mins between 10 PM and 6<br>AM; 10 mins between 6 AM and 10<br>PM|

Source: Authors’ elaboration.

50
to the United States and Western Europe, respectively, in the year 2000.<sup>16</sup> As shown, the principal costs of these drug industries are associated with distribution rather than production. One pure kilogram of cocaine exported from Colombia in 2000 cost traffickers $1,050; of this amount, $650 covered farmers’ cultivation costs. However, traffickers priced this same kilogram of cocaine for U.S. importers at $23,000. And, moving down to the retail level (through perhaps four transactions), the kilogram fetched $120,000 from consumers, sold in 500 milligram units at 50% purity. The story with heroin was similar: A pure kilogram of heroin produced in Afghanistan for less than $1,000, was exported from Turkey for $10,000, and by the time it reached consumers in Western Europe it was priced at $175,000.

The figures in Table 6 suggest three general propositions:

- The cost of production, as opposed to distribution, is a trivial share of the final price.  That statement holds true even if one adds the cost of refining to that of growing coca leaf or opium poppies.

- Smuggling, which is the principal transnational activity, accounts for a modest share but much more than production and refining.

- The vast majority of retail prices in Western markets are accounted for by domestic distribution in the consumer country.  Most of the domestic distribution revenues go to the lowest levels of the distribution system.  If the retailer and lowest level wholesaler each raise their purchase price by 75 percent, which until recently was a low estimate of the margin, they account for two thirds of the final price.

|**Stage**|**Cocaine**|**Heroin**|
|---|---|---|
|Farm-gate|$650 (Leaf in Colombia)|$550 (Opium in Afghanistan)|
|Export|$1,050 (Colombia)|$2,000-4,000 (Afghanistan)|
|Import|$23,000 (Miami)|$10,000 (Turkey export)|
|Wholesale (Kilo)|$33,000 (Chicago)|$50,000 (London)|
|Wholesale (Oz)|$52,000 (Chicago)|n.a.|
|Retail (100 mg. pure)|$120,000 (Chicago)|$175,000 (London)|

Source: Drug Enforcement Administration; EMCDDA; UNODC; n.a. is “not available” production prior to the 1979 Islamic Revolution is only fleetingly mentioned; see for example Booth (1996). 16 I have chosen 2000 rather than 2002 because Afghanistan’s opium price has been at an historic high following recovery from the cutback in production in 2001 and is now falling back closer to the levels of the late 1990s.

11
study wave, participants were assigned to attend a particular class that met at the same time every week for the duration of the training program. Classes consisted solely of either financial education training participants or health training participants. In estimating equations (1) and (2), we cluster standard errors at the wave-class level.

# _3.2 Summary Statistics and Randomization Checks_

Baseline characteristics for our sample are presented in Table 2. Households in our sample comprised 6 members on average, with a mean monthly income of Rs. 7017 (US$ 120). A little more than half (58 percent) of our respondents were female, and a vast majority was married. Respondents in our sample also had limited schooling, with 47 percent having completed elementary school, but only 4 percent having completed secondary school.

In addition to standard data on household demographics and respondent characteristics, our baseline survey measured financial knowledge, attitudes, and preferences. First, we note that almost everyone in our sample (94 percent) reported having difficulty saving. Next, we measured discount rates in the standard manner, by asking respondents to provide the minimum amount they would be willing to hypothetically accept in one month in lieu of a hypothetical payment of Rs. 350 today. Respondents in our sample reported relatively high monthly discount rates: the median was 0.14, while the average was 1.52. We also measured risk aversion by allowing respondents to choose between a payment of Rs. 10 with certainty, or playing a lottery that pays out Rs. 25 or Rs. 0 with equal probability. 18 percent of our sample chose the safe payment, and these respondents were coded as risk averse.

We also measured basic computational skills through a series of eight mathematics questions. The mean score for these mathematics questions was 4.73 out of 8. We find similar computational skill levels as in Cole, Sampson, and Zia (2011) in Indonesia. Specifically, almost all respondents could answer a simple addition question (“How much is 4+3?”), but only about 50 percent was able to answer a multiplication question correctly (“What is 3 multiplied by 6?”). Even fewer respondents were able to make percentage calculations correctly (“What is 8 percent of 100?”), with close to half responding “do not know” to this question.

15
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Table 1: Educational attainment of refugees and Greek natives

|||Male|||Female||
|---|---|---|---|---|---|---|
||Natives<br>(1)|Refugees<br>(2)|Diff<br>(2)-(1)|Natives<br>(1)|Refugees<br>(2)|Diff.<br>(2)-(1)|
|**Panel A: 1928 Cen**|**sus**||||||
|Literacy|0.751|0.666|-0.085***|0.336|0.355|0.019***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|N|1,301,237|263,461|1,564,698|1,342,233|287,981|1,630,214|
|**Panel B: 2001 Cen**|**sus**||||||
|Literacy|0.919|0.931|0.013*|0.688|0.788|0.100***|
||(0.002)|(0.007)|(0.008)|(0.003)|(0.009)|(0.011)|
|Primary school|0.693|0.706|0.014|0.431|0.497|0.066***|
||(0.004)|(0.013)|(0.013)|(0.004)|(0.012)|(0.012)|
|Secondary school|0.137|0.189|0.052***|0.053|0.103|0.050***|
||(0.003)|(0.011)|(0.010)|(0.002)|(0.007)|(0.006)|
|N|14,371|1,270|15,641|18,325|1,890|20,215|
|**Panel C: European**|**Social Surv**|**ey**|||||
|Primary school|0.817|0.843|0.026|0.660|0.671|0.011|
||(0.015)|(0.033)|(0.038)|(0.015)|(0.039)|(0.043)|
|Secondary school|0.293|0.256|-0.037|0.136|0.194|0.059*|
||(0.017)|(0.040)|(0.045)|(0.011)|(0.033)|(0.031)|
|Tertiary education|0.076|0.050|-0.027|0.021|0.069|0.048***|
||(0.010)|(0.020)|(0.026)|(0.005)|(0.021)|(0.015)|
|N|693|121|814|944|143|1,087|

**Notes** This table shows the average literacy and education of refugees and Greek natives in different dataset. In Panel A, the sample consists of individuals born in 1858-1907, i.e., 16-65 adults in 1923. In Panel B, the sample consists of individuals born in1908-1922, i.e., 0-15 children in 1923. In Panel C, the sample consists of secondgeneration refugees and natives’ offspring born in 1923-1950. Standard errors in parentheses. *** _p <_ 0.01, ** _p <_ 0.05, * _p <_ 0.1; Source: Author’s elaboration on the 1928 and 2001 Greek population censuses and European Social Survey (ESS).

29
**Table 2: Relationship between the employment rate of older persons (55-64) and labor market outcomes of prime-aged persons (25-54)**

||(1)<br>Prime|(2)<br>Prime|(3)<br>Prime|(4)<br>Prime|(5)<br>Prime|
|---|---|---|---|---|---|
||Employment|Unemployment|Hours of work(log)|Hourlywage(log)|Monthlyincome(log)|
|Employment rate of old persons|0.679***|-0.081***|0.000|-0.005**|-0.004**|
|(55-64)|(0.044)|(0.010)|(0.001)|(0.002)|(0.002)|
|Natural log of hourly income of|3.209**|-0.062|-0.057***|||
|prime-aged persons (25-54)|(1.319)|(0.335)|(0.021)|||
|Unemployment rate|||0.005*|0.001|0.016**|
||||(0.003)|(0.007)|(0.008)|
|Poverty rate|-0.068*|0.061***|-0.001**|0.001|0.003|
||(0.041)|(0.009)|(0.001)|(0.002)|(0.002)|
|Share of employment in the|-0.087***|-0.001|-0.004***|-0.003*|-0.005***|
|agriculture sector|(0.027)|(0.008)|(0.000)|(0.001)|(0.001)|
|Share of employment in the|-0.179***|0.050***|-0.001|-0.005**|-0.002|
|industrial sector|(0.036)|(0.014)|(0.001)|(0.002)|(0.002)|
|Share of prime-aged persons|0.044|0.047|0.001|0.032***|0.029***|
|(25-54)|(0.078)|(0.030)|(0.001)|(0.005)|(0.005)|
|Share of population with a primary|0.082***|-0.047***|0.001*|-0.003*|0.000|
|education or less|(0.023)|(0.007)|(0.000)|(0.001)|(0.001)|
|Natural log of GRP per capita|-0.055|-0.047|0.041***|0.116***|0.169***|
|(constant 2010 IDR)|(0.371)|(0.109)|(0.006)|(0.022)|(0.022)|
|Growth in GRP per capita|-0.010|-0.014|0.001|-0.002|-0.000|
||(0.043)|(0.009)|(0.001)|(0.002)|(0.003)|
|Number of observations|272|272|272|272|272|
|R-squared|0.766|0.737|0.792|0.741|0.770|
|Year dummies|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

18