rafmacalaba/datause-encoder-data huggingface dataset:
_46_ _Private Sector Approaches to Effective Fandly Planning_ enough country-specific information to permit impact evaluation of at least national-level projects.

# 7. Bibliography

- Akin, J., N. Birdsall, and D. De Ferranti. 1987. Financing Health Services in Developing Countries: An Agenda for Reform. Washington, D.C.: World Bank.

- AMICO Assistencia Medica. 1987. Assessing Costs and Benefits of Incorporating Family Planning Services Delivery into a Prepaid Health Maintenance Organization in Brazil. Final report to the Population Council, INOPAL Project.

- Arruda, J. M., A. M. Thome, and L. Morris. 1982. _Pesquisa sobre Saude Materno-lfantil e Planejamento Fanmliar: Piaui-1982._ Brazil: Sociedade Civil Bem-Estar Familiar no Brasil Rio de Janeiro (BEMFAM).

- Bertrand, J. T., R. Santiso, R. J. Cisneros, F. Mascarin, and L. Morris. 1982. Family Planning Communications and Contraceptive Use in Guatemala, El Salvador and Panama. _Studies In Family Planning,_ 13, 190-199.

- Brown, L. D., and D. C. Korten. 1989. Understanding Voluntary Organizations: Guidelines for Donors. Working Paper 258. Washington, D.C.: World Bank.

- Brown, V. W., M. Friedman, B. Janowitz, and T. Levy. 1988. Evaluation of the Social Marketing for Change (SOMARC) Project International Science and Technology Institute, Inc. Arlington, Va.

- Bruns, B., and W. P. McGreevey. 1988. Brazil Public Spending on Social Programs: Issues and Options. Report 7086-BR, World Bank.

- CEPAR. 1989. Efectos de la Disponibilida **d** e Anticonceptivos en la Regulacion de la Fecundidad en el Area Rural del Ecuador. Quito, Ecuador.

- Cernea, M. M. 1988. Nongovermnental Organizations and Local Development. World Bank Discussion Paper No. 40. Washington, D.C.: World Bank.

- Chernichovsky, D., and J. Anson. 1989. Cost Recovery and True Cost-Effectiveness Ratios of Contraceptive Provision. Draft report, World Bank.

- Cochrane, S., J. Hammer, B. Janowitz, and G. Kenney. 1990. The Economics of Family Planning. Washington, D.C.: World Bank.

- Coeytaux, F. M. 1988. Induced Abortion in Sub-Saharan Africa: What We Do and Do Not Know. _Studies In Family Planning,_ 19, 186-189.

Consultora 1/2 de Marketing. 1989. Microgynon. Lima, Peru.

- Cross, H. _E.,_ V. H. Poole, R. E. Levine, and R. M. Cornelius. 1901. Contraceptive Source and For-Profit Private Sector in Third World Family Planning: Evidence and Implications from
# **Table A6. Impact of living in the GMA and, once in the Kafue Park System GMA, of -** **<u>participating in the VAG/CRB on the non poor households</u>**

||Kafue|Treatment Regres<br>capita co|sion mod<br>nsumptio|els for the impact<br>nexpenditure of|on per|
|---|---|---|---|---|---|
|||||Participating|in|
|Variable|Variable description|LivinginGM|A<sup>_a_</sup>|<br>VAG/CRB|<br><sup>_b_</sup>|
|||(1)||(2)||
|Hage|Age of the household head|-0.001<br>(0.002)||0.000<br>(0.002)||
|fhhh|Female-headed dummy|-0.238|***|-0.442|***|
|||(0.078)||(0.072)||
|maxedu|Education of the most educated member|0.028|**|0.016||
||(years)|(0.011)||(0.014)||
|c0to14|Number of children less than 15 years old|-0.125<br>(0.016)|***|-0.135<br>(0.021)|***|
|f15to60|Number of female members 15-60 years|-0.003<br>(0.054)||-0.050<br>(0.028)|*|
|m15to60|Number of male members 15-60 years|-0.079<br>(0.035)|**|-0.122<br>(0.045)|***|
|m61plus|Number of members older than 60 years|-0.152<br>(0.058)|***|-0.329<br>(0.067)|***|
|kroad|Distance to the nearest main road in km|0.004<br>(0.002)||0.000<br>(0.002)||
|kbsch|Distance to the nearest basic school in km|0.002<br>(0.007)||0.008<br>(0.013)||
|kheal|Distance to the nearest health centre in km|-0.004<br>(0.005)||-0.008<br>(0.009)||
|vacast|Value of consumer durable assets in ZMK|0.020<br>(0.012)|*|0.448<br>(0.128)|***|
|dcoop|Participation in cooperatives dummy<br>|0.170<br>(0.118)||0.235<br>(0.216)||
|gma or|Participation dummy variable<sup>_a,b_</sup>|-0.445||-0.287||
|dvcrb||(0.389)||(0.231)||
|cons|Intercept|13.792|***|13.860|***|
|||(0.148)||(0.172)||
|Number of|observations|612||166||
|Log-likelih|ood value|-862||-167||
|Goodness|of fit Chi-Square|249.10||||
|Rho||0.51||0.13||

> _a_ Participation dummy variable refers to the GMA dummy, equal to 1 if the household is located in a GMA

> _b_ The participation dummy variable refers to the CRB/VAG dummy, equal to 1 if the household participates in the VAG/CRB. This model uses a sub-sample of households that are located in the GMA Significance: *=Significant at 10%; **=Significant at 5%; ***=Significant at 1% Values in parentheses are standard errors

21
# **1 Are the impacts of global urban flooding on the rise? 1.1 Introduction**

# **Urban flooding is an increasingly important issue which may shape the destinies of**

**whole cities or substantially change the face of them for decades to come.** Major urban flooding in the last decade affected Mumbai, New Orleans, Yangon and Dresden, but smaller scale events, some regular and repeated in the same populations, can be just as disruptive to the affected areas (IFRC 2010). Two major global themes lead us to believe that the number and scale of impact of flood events will continue and possibly accelerate in the next 50 years. The first is the global trend in urbanization which is a defining trend of the early 21<sup>st</sup> century, in particular the growth in low to middle income developing countries. This rapid growth often leads to poorly planned urbanization making urban populations increasingly vulnerable to floods. The second is environmental change as the climate warms, sea levels rise and extreme weather events are more frequent. This has led to an anticipation of much higher risk of flooding in the future.

Urban floods have specific characteristics. This paper is concerned with urban flooding and the negative impacts that large and small flood events have on the populations of cities and towns. Urban settlements can be affected by all types of flood event, from the regular seasonal swell of a river through intense rainfall flash flooding, groundwater flooding, coastal storms, and coastal erosion and subsidence. As urban settlements encompass the major economic and social hubs of any national population, urban flooding is often regarded as of more moment than rural floods and typically causes massive damage and disruption beyond the scope of the actual floodwaters.

Urban flooding is a global phenomenon which caused devastation and economic losses and will continue to do so. According to the Centre for Research on the Epidemiology of Disasters (CRED), flooding in 2010 affected 178 million people and amongst all natural disasters the occurrence of floods is the most frequent. In the last century based on International Strategy for Disaster Reduction (ISDR) statistical analysis, the total numbers of hydro-meteorological events was 7,486.

In 2010, the devastating consequences of the floods in Pakistan were covered by the press worldwide. At the start of 2011 we saw the spectacle of the Queensland flooding in Australia, as well as destructive floods and landslides around Rio de Janeiro in Brazil. In addition, and underreported, both South Africa and Sri Lanka saw large-scale floods in the period. These flood events have hit developed and developing nations alike but are often observed to have a disproportionate impact on the poor and socially disadvantaged who are least able to help themselves (ActionAid 2006). As such, urban flood risk management should already be a high priority for policy makers, city managers and urban planners. The additional questions discussed here are whether, as many policy makers are coming to believe, flooding is a growing risk for urban areas worldwide; if so, 1
<!-- Start of picture text --> Figure 1: Elasticities of TFR 1985, with respect to Female<br>Secondary Enrollments, 1970 and FP Services, 1982<br>Elasticities of TFR<br>0.6<br>-0- Fem Ed.(indep.) o FP Service (indep.)<br>0.4 -<br>0.2<br>0I<br>-0.2<br>-0.4<br>-0.6 0 10 L . 20 30 I i 40 i _ 50 60 70 I 80 I 90 I X 100<br><!-- End of picture text --> Levels of female sec. enrollment, 1970 Note. Elasticities computed from eq.1 Table 1, holding all other variables<sup>constant at their mean value.</sup> I... (Mean.s: Female Sec. Enroll. (1970). 13%, Family Planning Service Score (1982),<sup>24%, TFR (1985), 5.4).</sup>
and living with their spouse. We focus on respondents with at least junior secondary school education as research has shown that women with this level of education have the most discretion over whether they work or not and so are more likely to be able to have their behavior influenced.<sup>8</sup> The sample was constructed such that 75% of both male and female respondents were high school educated and 25% tertiary educated (to roughly reflect the coverage of these groups in the Indonesian population). The survey collected demographic information (including age, gender, number of children, own and spousal work status) and information on respondents’ attitudes and perceptions of social norms.

Specifically, we collected information on personal attitudes in relation to the extent of support for a) female labor force participation: “Are you supportive of married women with children under 12 working for pay outside the home?”; and b) sharing of childcare between husband and wife: “How supportive are you of husbands sharing day-to-day childcare duties with their wives?”.

We also elicited injunctive norms on what proportion of married men and married women are supportive of the above behaviors. These estimates were incentivized by paying approximately USD17 to the respondents who made the most accurate estimate. The difference between the mean of the sample’s reported level of support and the individually reported perception of social norms in both domains generates a measure of the extent of misperceptions about the social norm.

We also collected information on which people’s opinions are important to respondents when making decisions about female household members working and sharing childcare. Respondents were asked to indicate the extent to which they were concerned about the opinions of husbands, parents, parents-in-law, extended family members, people in their social networks and people in their religious community. Finally, we enquired about the potential sanctions individuals would face if they decided to deviate from the social norm.

Our second online survey collected similar information from 4,478 similarly selected respondents. The information intervention was embedded in the survey just prior to survey exit. The intervention is detailed further below in Section 4.<sup>9</sup>

# **3 Social Context**

Female labor force participation in Indonesia has remained relatively constant over the – past two decades with around 50% of women working even with high economic growth, concomitant large increases in women’s educational attainment and the service sectors

> 8Women with upper secondary education have the lowest female labor force participation in Indonesia, Cameron et al. (2019).

> 9Ethics approval for the surveys and interventions was obtained from the University of Melbourne (202223161-28577-5) and University of Indonesia (LPEM FEB - 14/UN2.F6.D2.LPM/PPM.KEP/2022). The trial and pre-analysis plan were registered with the AEA RCT Registry (AEARCTR-0009493). Unless otherwise indicated, the analysis presented follows that laid out in the pre-analysis plan.

6
when unemployment rates are low there should be more job opportunities available, making it easier for people to use their training to find jobs. But in such cases firms facing labor shortages might hire workers regardless of whether or not they have been trained. In contrast, when unemployment rates are high, if employers are not hiring then having new skills may not help the unemployed find jobs, but on the other hand, employers may be choosier and so training may offer workers a way to distinguish themselves from other workers competing for the same jobs.

As a result, theoretically it is ambiguous whether we should expect training to have more or less impact in situations of higher or lower unemployment. Consistent with this, there are mixed results in the existing (non-experimental) literature. Lechner and Wunsch (2009) find German training programs to be more effective when carried out during times when unemployment rates are higher and Kluve (2010) finds in his meta-analysis that program effects tend to be higher when unemployment rates are higher. But using Norwegian data, Raaum et al. (2003) find training impacts to be positively correlated with post-training employment rates in local labor markets.

The training programs in our study took place throughout 23 provinces with a variety of unemployment conditions – at the time of training, unemployment rates ranged from under 5 percent to over 20 percent, with a median of approximately 14 percent. The third to last row of Table 9 interacts the treatment with an indicator for above median provincial unemployment. The point estimates for our survey outcomes are positive, suggesting larger treatment impacts when labor markets are tighter, but not statistically significant. The longer-term impact is statistically significant, suggesting training to increase employment by 4.9 percentage points more when done in high unemployment regions compared to low unemployment regions, although this result does not survive a multiple hypothesis test correction.

Finally we examine heterogeneity with respect to the type of course. There is some evidence that accounting courses, the most common course type, have larger impacts on short-term employment outcomes than other types of courses. This impact is however not statistically significant in terms of formal employment, or long-term formal employment.

# **7. What distinguishes private courses from public courses?**

The main source of impact heterogeneity in course characteristics is thus whether the course is operated by a private or public provider. Since individuals were only randomly assigned within courses, and not across courses, this might capture differences in the types of individuals 22
3 Table 1: TERMS-OF-TRADE, REAL INTEREST RATES AND BORROWING SOURCES

||1980|1981|1982|1903|1984|1955|<br>1988|
|---|---|---|---|---|---|---|---|
|Change in Terms of Trade (%):||||||||
|Non-Fuelexporters|-5.8|-4.0|-2.0|0.8|2.6|-2.3|<br>-2.9|
|Fuel exports|44.1|11.1|0.3|-8.6|_0.7_|-2.5|-47.9|
|Real interest rate a/|4.4|6.5|8.7|58|7.0|5.0|5.0|
|Borrowingsources||||||||
|**Netprivate**borrowing b/|73.3|72.6|48.9|25.4|14.8|1a.7|<br>2.3|
|Official borrowing (IMF and WS)||||||||
|Number of WB Adjustment loans<br>(amount b/)|8<br>(0.**)**|8<br>(0.9)|5<br>(0.8)|22<br>(3.4)|10<br> (1.1)|18<br>(2.0)|28<br>(3.9)|
|Number of IMF loans<br>(net IMF credit b/)|26<br>(2.3)|32<br>(4.7)|30<br>(3.8)|41<br>(9.4)|30<br>(4.4)|31<br> (0.9)|32<br>(-1.4)|

Notes: a/ Percent.

b/ Biion current dollars.

Sources: World Bank (1989), table 2.1, and Khan (1988, appendix).
controlled for by industry ...xed e¤ects in the regressions.

# 1.1.3 Computing Upstream Input Tari¤s

Computing an industry’s upstream tari¤s involves two steps. For each upstream industry, input tari¤s are measured as a weighted average of tari¤s facing all input suppliers to that industry. Speci...cally, we obtain the share of industry i’s inputs in total material cost of industry j, sij, from the Chinese IO table for 2002. Then for each industry j, we compute e the weighted average of input tari¤s as � jt =<sup>PI</sup> i=1<sup>sij�it,where�itistheaveragetari¤ rate</sup> for industry i in year t and I is the total number of industries. Finally, for each downstream industry k, we use the IO coe¢ cients again to compute the weighted average of upstream input tari¤s e�<sup>U</sup> kt<sup>=PI</sup> j=1<sup>sjke�jt.TheideatousetheIOtablestwiceisthatweneedthe</sup> measure of tari¤s facing domestic input suppliers, not downstream exporters. For example, a garment ...rm uses fabrics, zippers and buttons. Fabrics ...rms use cotton yarns, zipper ...rms use steel, and button ...rms use plastics. Thus, the upstream input tari¤ for a garment ...rm is a weighted average tari¤ rates on cotton yarns, steel and plastics.

# 1.1.4 Computing Industry-speci...c Exchange Rate Indices

We use the Tornqvist method to construct an industry-speci...c time-varying exchange rate. For each industry j, let Ijt be the set of common countries ...rms in industry j import from in two consecutive years, t and t � 1: Denote country c’s currency price of a yuan in year t and t � 1 by Ect and Ect�1; and denote country c’s shares in industry j<sup>0</sup> s total imports in year t and t � 1 by scjt and scj;t�1. The industry-speci...c rate of yuan appreciation with respect to the countries from which industry j imports in year t is de...ned as 4
42 other locationally specific policies. However, the household can only choose to change its location of residence in response to policy. There are no modal options, including car pooling, in the model, and no decisions on vehicle ownership. As an analytical rather than an econometric model, the choice of parameters is ad hoc.

Monocentric models are surely a poor representation of modern U.S. cities that have many satellite centers and much reverse and cross-town commuting. They may be much better at characterizing new cities in developing economies (but not large areas, such as Mexico City). The model could easily be updated with new emissions data and illustrates how to set the appropriate parameter values.24 Kim (1979). A less drastic simplification of space is provided by the Kim model, representing the class of urban land rent models that attempt to make the spatial representation of the city somewhat more realistic by allowing for polycentricity. Kim does this with a linear programming model of an efficient city. The city is divided into four mirror image quadrants, each divided into squares. The model focuses primarily on goods transport, both within the ci:y and for export, minimizing an objective function with costs of production, costs of commercial and household transportation, and costs of exporting as main components. In spite of the focus on commercial transport and in contrast to the McConnell-Straszheim model, multiple commuting modes are modeled. Households are assumed to make only work trips, however, and there is no congestion in the model.

24. Fiveral key parameters require information on the benefits of reducing emissions. More and better information is available on this issue than when the McConnell study was written.
24 benefits urban areas more than rural areas but the former are also taxed more regularly and heavily for social security. Although the rich Southeastern part of the country gets a disproportionate share of social security and medical benefits residents of that region pay an even larger share of taxes, so on balance the system redistributes to the poor Northeast (see World Bank, 1988). This is consistent with predictions from public choice theory that influential middle and upper income groups will benefit disproportionately from public spending, but some redistrib3ution to poor groups or regions will nevertheless take place.

# Social Insurance versus Redistribution

Fourth, many programs that look redistributive from an ex post (or transitory income) point of view are really insurance from an ex ante (or permanent income) point of view. For example, unemployment or disability compensation are received by groups with lower temporary income, but much of this transfer is an insurance pay-off in exchange for the earlier payment of insurance premiums by these same groups. It represents a smoothing or maintenance rather than a redistribution of expected lifetime income. Redistribution is correctly measured by the difference between premiums paid and expected insurance returns which may be positive for some and negative for others but is far less than total transfers for all.

We digress for a moment to comment on the relationship between social insurance and redistribution. All social insurance programs have some elements of risk-pooling and some elements of redistribution, since some people are actuarially more vulnerable than others but in a public program premiums are constrained to be relatively uniform and do not reflect differential riskiness. Indeed, one reason for operating such insurance
Table 4. Program impact on management

|Dependent variable|Absence rate|Sta¤present|Condition of clinic|
|---|---|---|---|
|Speci...cation|(1)|(2)|(3)|
|Program impact|-0.10*|0.78*|1.13***|
||(0.058)|(0.46)|(0.31)|
|Mean control group|0.53|2.3|-0.52|
|District ...xed e¤ects|Yes|Yes|Yes|
|Observations|50|50|50|
|R<sup>2</sup>|0.35|0.30|0.47|

a. *** [**] (*) denote signi...cance at the 1 [5] (10) percent level. b. Speci...cation: (1) Absence rate is the ratio of workers not physically present at the time of the postintervention survey to the number of workers employed; (2) Sta¤ present is the number of workers (veri...ed by the enumerators) to be present at the time of the (surprise) post-intervention survey; (3) Condition of clinic is the ...rst component from a principal components analysis of the variables "condition of the ‡oors of the health center", "condition of the walls", "condition of furniture", and "smell of the facility". Each condition is ranked from 1 (dirty) to 3 (clean) by the enumerators. c. Robust standard errors in parenthesis.

46
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to compare Japanese-Brazilians to the average Brazilian in the PNAD national surveys. A Nikkei worker in the fifth decile of the Nikkei wage distribution would be in the seventh decile of the São Paulo-Parana distribution and eighth decile of the overall Brazilian wage distribution. Mean wages of Nikkei wage workers are 1440 reais per month, approximately $US720.

The survey was designed to provide detail on the characteristics of households with and without migrants, estimate the proportion of households receiving remittances and with migrants in Japan, and examine the consequences of migration and remittances on the sending households. We compare the performance of the three different survey methods in meeting these objectives. The same questionnaire was used for the stratified random sample and snowball surveys, and a shorter version of the questionnaire was used for the intercept surveys. Therefore we can directly compare answers to the same questions across survey methodologies, and determine the extent to which the intercept and snowball surveys are able to give similar results to the more expensive census-based survey, and test for the presence of the types of biases one might expect. For example, we would expect individuals who belong to Nikkei community organizations to have a greater connection to Japan, and therefore to be more likely to migrate. Nikkei who are more integrated into Brazilian society may be harder to observe through snowball and intercept surveys. We will compare across the three surveys the characteristics of migrant sending households, the likelihood of receiving remittances and level of remittances received, and the incidence of return migration.

Several characteristics of the Nikkei population in Brazil present a challenge for surveying. Firstly, the population is predominantly urban, with many living in high-rise apartments secured by building managers or doormen. With crime a general concern in urban Brazil, some building managers are reluctant to allow entry into apartment buildings. Moreover, as is common in urban areas, most individuals work outside of their homes, and many are reluctant to be interrupted at home outside of working hours. Secondly, the Nikkei population in Brazil share the characteristic of many ethnic minorities and migrant groups of being suspicious of outsiders. Furthermore, there have 10

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