rafmacalaba/data-use-mentions-extended huggingface dataset:
high-potential countries. This is simply because these countries represent large markets with significant potential returns on investment in the long run. But public-private partnerships will also survive the current crisis because few developing countries will be able to address their extraordinary infrastructure needs from public resources alone.<sup>2</sup> Ultimately, the disagreement should probably be as to how fast the private sector will return to some countries and under what conditions.

When the private sector returns in larger numbers, it will indeed do so in very different forms from those we have observed in the 1990s. Their willingness to accept risk will be limited. This will require new contractual arrangements with different levels and types of risksharing arrangements. It will also imply new actors, including non-OECD (Organization for economic cooperation and development) actors willing to compete in risky environments they are more familiar with than OECD operators—e.g. South African infrastructure firms are increasingly present throughout Africa, Malaysian firms in Asia and Africa, Brazilian and Mexican firms throughout Latin America.

The new business models also demand better governance in business practices. An improvement in the accountability of all stakeholders in public-private partnerships will require much more transparency in the analytical and quantitative assessments of deals. These

> 1 This paper extends to all developing countries of  part of a database put together for Latin America by Sirtaine et al. (2004) for Latin America but covers a much shorter period and a lower number of return indicators. 2 For the poorest countries unable to attract private investment, the alternative is likely to be grants from richer countries.

1
501|0.055|0.199|0.511|18.718|2.974|
|Sample Size|1,967|2,018|2,049|821|1,966|2,019|2,049|1,966|2,019|2,049|998|2,048|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. We correct for inconsistencies in 'ever married' and 'ever pregnant' across rounds.All regressions are weighted to make them representative of the target population in the study EAs. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to respondents who were surveyed during the latest household survey conducted two years after the program (Round 4). Parameter estimates statistically different than zero at 99% (***), 95% (**), and 90% (*) confidence.
1 May 1<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br>Daily severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization. Note: The daily severity ratio is the daily equivalent of the cumulative severity ratio. Horizontal line shows nth cause of death above or around the maximum daily severity ratio. Cut-off date for all data is May 23.

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in 1998, over a quarter were more than 50 percent above the poverty line in 1997.6 While some of this must represent measurement error, nevertheless since in agriculture and informal occupations the variability of income is often very high, there is no question that over a period of 5 to 10 years many households which are not CCED poor will experience one or more _episodes_ of CCED poverty.

The question is how to measure this vulnerability, beyond merely pointing out that those near the poverty line are "vulnerable." There are two possible ways forward, both of which require measuring expenditure variability at the household level, or at least amongst types of households (e.g. urban-rural, formal-informal, education level, sector of occupation, etc.). One is to regard households expenditures as a dynamic process, with both the mean (,u) and variability (a): _J) ei (t j =_ **_ei (p i, ,i )_** Then the probability of at least one episode of CCED poverty in the next, say, five years is:' _6) P({]ttE{t,t+5}:e 1 (t) < PL(t)})_

- **6** See Skoufias _et al_ (2000).

- 7 This is discussed further and applied to Indonesian data in Pritchett _et al_ (2000).

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provision dummies indicating the presence of each policy area in RTAs by country-pair-year, and take the maximum value of each provision dummy across multiple RTAs (if any) within each country-pair-year combination.

As a second step, we construct several measures of RTAs’ depth based on the provisions included in the trade agreement that each country pair shares in a given year. Specifically, we consider different count variables, differentiating by type of provision: (i) number of provisions (independently of their legal enforceability), (ii) number of legally enforceable provisions (i.e. whose implementation is supported by strong legal language and by the availability of a dispute settlement mechanism), (iii) number of WTO-plus provisions (WTO+) – i.e. provisions covered by the current mandate of the WTO, (iv) the number of WTO-extra provisions (WTO-X) – i.e. provisions not covered by the current mandate of the WTO, and (iv) number of core provisions – i.e. provisions directly related to trade enhancing factors.<sup>4</sup> While in our empirical exercise, we use all these proxies for the depth of RTAs, our baseline measure of RTA depth is the count of legally enforceable provisions in each RTA.<sup>5</sup> The simple count of provisions in RTAs gives equal weight to each clause included in the agreement, and one may want to give more weight to “rare” provisions (i.e. provisions included less frequently in RTAs and likely to signal stronger market integration). To address this concern, as a robustness check, we also use alternative measures of RTA depth based on the weighted sum of provisions (using one minus the frequency to which each provision appears in the 300 RTAs covered by the DTA database).<sup>6</sup> One drawback of the DTA database is that it does not cover RTAs that are no longer in force.<sup>7</sup
significant change in the sex-composition of first births by socio-economic group after ultrasound technology became available.

Exogeneity of firstborn sex has also been previously defended (Das Gupta and Bhat (1997), Visaria (2005), Bhalotra and Cochrane (2010)). It also lines up with recent survey data that suggest that parents do not always prefer having a son over a daughter. Jayachandran (2016) finds that although the vast majority of families want to have a son if they can only have one child, at a family size of two they prefer having one daughter and one son over having two sons. As desired and actual fertility in India are well above one (Table A.5), it is reasonable to assume that parents are not averse to having one girl, despite a strong desire for at least one boy. In our sample, on average, desired fertility is 2.7; the desired number of sons and daughters are 1.38 and 1.01, respectively.

**Pre-trends not different by firstborn sex.** In order to test for a significant difference in trends in under-5 mortality and health investments for firstborn-boy and firstborn-girl families in the pre-ultrasound period, we restrict the sample to the pre-ultrasound period, 1973-1984, and regress the outcomes of interest on the full set of interactions between indicators for firstborn girl ( _Gj_ ), female birth ( _Fi_ ), and year of birth, with and without fixed effects for birth order and state, and examine the coefficients of the triple-interactions. The coefficients from these regressions are presented in Figure 4 which demonstrates no significant divergence between EFM, immunization, and breastfeeding in firstborn-boy and firstborn-girl families for pre-ultrasound cohorts for children of parity greater than one. The same is true for pre-trends estimated separately for the sub-samples of boys and girls (see Figure A.1).

**Firstborn sex predicts sex-selection at higher parities.** Previous studies, namely Bhalotra and Cochrane (2010) and Rosenblum (2013), have established that parents randomly exposed
wage rates.'" Thus, while the provinces with higher labor costs can be expected to compete less favorably in their efforts to attract foreign investment, our expectation is that this variable is not likely to yield a strong negative relationship with provincial FDI accumulation. The measure of labor cost we use is the average annual provincial wage of staff and workers in 1992.

_Human capital._ A host region's labor supply influences foreign investors' location decisions not only in terms of input costs, but also through the quality of the skills of the laborforce--especially if the price mechanism is repressed, as is the case in China's labor markets. All other things equal, locales with highly skilled workers--most easily measured by education levels--would be expected to compete more favorably than others in their FDI attractiveness. In this study, adult illiteracy is taken to represent the extent of basic education of a province's workers. The data are taken from China's Fourth National Population Census in 1990, where the population is classified as illiterate and semi-illiterate. These two classifications are divided by provincial population "up to age 6" and "over age 6" to obtain an illiteracy ratio.

_Infrastructure._ There is no dispute that the extent of an area's infrastructure development is important in an investor's location choice. Infrastructure of course covers many dimensions, ranging from highways to railroads to telecommunication systems to even institutional development (e.g., extensiveness of business-related services, such as accounting, legal services, etc.). Owing to the difficulty of capturing all these various dimensions in an easily calculable variable, for this study we settled on using as a measure of a province's infrastructure development the total length of transportation routes within the province, calculated as the sum of the length of the (i) railways in operation, (ii) the navigable inland waterways and (iii) the constructed highways in 1992, normalized by provincial geographical size. In
WPS3926

# **AN ANALYSIS OF MONEY’S WORTH RATIOS IN CHILE**

## **Craig Thorburn, Roberto Rocha, and Marco Morales**<sup>**1**</sup>

### **Abstract**

Empirical analyses of annuities markets have been limited to a few developed countries and restricted by data limitations. Chile provides excellent conditions for research on annuities due to the depth of its market and the availability of data. The paper utilizes an extensive dataset on individual annuities to examine econometrically a measure of market performance – money’s worth ratios (MWRs), or the ratio of the expected present value of annuity payments to the premium. The results show that annuitants in Chile have generally got a good deal for their premiums, as indicated by MWRs higher than one and also higher than those estimated for other countries. The difference between Chile and other countries is striking considering that annuities in Chile are indexed to prices. The wide range of indexed instruments in Chile, allowing providers to hedge their risks while extracting higher returns, helps explain the difference. The high degree of market competition has also contributed to this outcome. Efforts to improve market transparency through a new electronic quotation system have decreased the dispersion of MWRs. Finally, MWRs tend to decrease for contracts with longer durations, reflecting pricing for higher longevity and reinvestment risks. These results are consistent with separate research on the annuity rate, and indicate the need to ensure competition and market transparency, as well as to develop appropriate financial instruments for providers in order to ensure good outcomes for annuitants.

### World Bank Policy Research Working Paper 3926, May 2006

_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
subject to one significant qualification, however. In PTAs between developing countries and major developed economies, such as the United States or the EU, the former may not, in fact, receive considerable gains in market access in the latter. This is because the developed countries typically already had low MFN tariffs and few quotas on imports of manufactured goods. In this context, developing-country partners may have anticipated potential gains in inward technology flows, through increases in inward FDI and offshoring. Evidence

> 10 This restriction implies that we do not include the pre-1995 EU/EEA agreements. However, because the World Bank database classifies each of the post-1995 EU enlargements (in 2004, 2007, and 2013) as separate PTAs, these are included in relevant treatment groups.

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# 1 Introduction

High rates of child absenteeism from school are a serious impediment to economic growth in the developing world. In 2000, an estimated 88 million children were out of school, most of them in Southern Asia and Africa.<sup>1</sup> One of the measures that has been taken by governments in countries like India, Bangladesh, Brazil, Swaziland or Jamaica to encourage school attendance is the provision of government-subsidized school meals.<sup>2</sup> But despite the popularity and cost of school meals, there is little evidence on their impact on school participation and educational achievement. In this paper, I quantify the e¤ects of subsidized school meals on these outcomes using data from a randomized school meals program in preschools in Western Kenya. The program was implemented in 25 preschools between 2000 and 2002, and provided a fully subsidized in-school breakfast on every school day to all pupils attending preschool. The 25 schools that received the school meals program were randomly chosen from a pool of 50 schools, which allows causal inferences to be drawn.

We ...rst estimate the impact of the breakfast program on school participation using an intention-to-treat estimator. The intention-to-treat estimator assigns children to the treatment or comparison group before the start of the program. Presence in any school is counted and assigned to the original treatment status of the child. In the presence of transfers between schools, this estimator is appropriate because it avoids upward bias in the following way: children who transfer from a comparison school to a treatment school are not counted as new enrollments in the treatment school and not counted as absences in the comparison school. Similarly, children who transfer from outside the sample schools are not counted. Children in the treatment group participated in school 35.9 percent of the time, versus 27.4 percent in the comparison group. The di¤erence is slightly less than one-third of the level in the comparison group. The program increased participation of both children who were previously enrolled (intensive margin
variables (IV) method and takes advantage of the historical data on slavery at the tribe level and the literature on the cultural heritage of trust.

The slavery data are from Nunn (2008a) and Nunn and Wantchecon (2011) who document the number of slaves exported by the ethnic groups during both the transatlantic and the Indian Ocean slave trade. I first match these historical data with the ethnic group of each respondent in the WVS. Then, following Nunn and Wantchecon (2011) I build a variable cap1
welfare-gains gains per country are given in Table 8.

The increase in real income associated with a reduction in the distance-effect for all trade flows is on average equal to 29 percent, ranging from over 80 percent for Brazil to -0.9 percent for Belgium, which currently gains from information frictions. Hence, our results suggest that potential gains from the reduction in information asymmetries brought about by online platforms are quite large. Unsurprisingly, as shown in Figure 9, the largest welfare gains would occur in remote countries.

# **6 Concluding Remarks**

Using a dataset on eBay cross-border transactions and comparable offline trade flows, we estimated a distance effect on trade flows about 65 percent smaller online than offline. Using various measures of information asymmetries at the product and country level, we argued this difference in distance effects was due to online technologies that reduce information and trust frictions associated with geographic distance. The largest distance reducing effects are observed where they are most needed, i.e., in countries which are little known, have corrupt governments, high levels of income inequality, little internet penetration and inefficient ports. This is promising in terms of the potential for technology to render trade more efficient and development friendly. Importantly, the welfare gains from the reduction in distance related trade costs are large. If information frictions offline were reduced to the level prevailing online, real income would increase by 29 percent on average.

From a development perspective, as online markets help overcome government and market failures, then a cheaper, quicker and perhaps more efficient way to integrate poor countries to world markets would be to provide sellers and buyers with the technical assistance necessary to participate in these online markets, rather than engaging in the uncertain and long processes of institution building.

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might be better able to negotiate their way past regulatory barriers and might find it easier to access government programs designed to encourage exports. In practice, the evidence for Africa is mixed.<sup>23</sup> The results from this study suggests that state-owned enterprises are significantly less likely to export

> 21 See Schiantarelli (1996) for a review of the literature on firm size and financial constraints.

> 22 Shirley and Walsh (2001) and Megginson and Netter (2001) survey the large literature on this topic, finding that private enterprises are more efficient than similar state-owned enterprises on average in low and middle-income countries.

> 23 Using data from the early 1990s, Grenier _et al_ . (1999) find that state-owned enterprises in Tanzania were more likely to export.  They note, however, that Bigsten _et al._ (1997) did not find similar results

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# **A Appendices**

## **A.1 Data: Share of Employment by Sectors**

The data on employment share by sectors have been taken from the WDI database which in turn are sourced from the ILO’s Key Indicators of the Labour Market database. The specific series for Agriculture, Industry and Services are _SL.AGR.EMPL.ZS_ , _SL.IND.EMPL.ZS_ and _SL.SRV.EMPL.ZS_ respectively. The annual data series for a country is often constructed using different sources for different years. However, as discussed before there are two serious concerns with regards to using the data as provided in the database. First, the source used for a particular year may not be representative with regards to geography, working age population, gender etc. Second, the source survey may have been conducted in a period that captures seasonal allocation of labor force rather than the usual allocation of labor across sectors. Both these factors are bound to interfere with estimation of sectoral gaps in productivity.

The challenge is to filter the spurious data out from the good data and to do this I proceed as follows. For each country, I first identify the years in which the employment share is constructed from a source that is free from any of the concerns listed above. For example, such a source should be nationally representative, should not exclude any age group within the working age population, corresponds to the sector of employment of workers’ usual activity etc. For the purpose of exposition, I name the filtered data points thus obtained as Consistent Data Points (CDP). In the next step, I identify which of the data sources for a particular country contains maximum CDP and term the series generated from this data source as the Longest Reasonable Series (LRS). In case the maximum CDP come from more than one data sources, I pick the one that corresponds to the most recent data. The LRS serves as the skeletal for including or excluding data points from other
Table 7 Aggregate Expected Loss from the Credit Allocation

||(1)<br>Total Credit<br>Program<br>(Million US$)|(2)<br>Total Credit<br>Program/GDP<br>(%)|(3)<br>Share of<br>Credit<br>Program<br>(%)|(4)<br>Default<br>Probability<br>(%)|(5)<br>Expected<br>Loss/GDP<br>(=(2)x(4))<br>(%)|(6)<br>_Ex Post_<br>Default<br>(%)|(7)<br>_Ex Post_<br>Loss/GDP<br>(=(2)x(6))<br>(%)|
|---|---|---|---|---|---|---|---|
|_Panel A: Risk Groups, Formal Firms_||||||||
|High Risk|607|0.2|7|18.2|0.04|17.1|0.04|
|Medium Risk|1,087|0.4|12|9.9|0.04|12.2|0.05|
|Medium-Low Risk|1,863|0.8|21|5.7|0.04|11.2|0.08|
|Low Risk|3,972|1.6|44|2.1|0.03|6.2|0.10|
|No Risk Data|1,411|0.6|16|18.2|0.10|17.1|0.10|
|Total: Formal Firms
To obtain quantitative estimates, we rely on a survey of Armenian firms undertaken for the study by Maliszewska _et al_ ., (2008) and data from the Cost of Doing Business study of the World Bank. We estimate that the costs of importing from and exporting to the EU will fall by 2.5 percent of production costs, while the costs of importing into Armenia from non-EU countries will fall by 2.3 percent of production costs and the cost of exporting to non-EU countries from Armenia will fall by 2.2 percent of production costs. Details of the estimation are available in appendix D.

# **Standards costs**

The EU devoted considerable resources to assisting its new member states with standards and, similarly it is allocating resources to this problem for the countries with which it may potentially have a DCFTA. Consequently, we assume these costs will fall as a result of a Deep and Comprehensive Free Trade Agreement. That is, for firms who will sell in the EU, after adaption by the firms and Armenian development of the National Quality Infrastructure, we assume the production costs of selling in the EU by Armenian firms will decline. We rely on a survey by Jakubiak et al., (2006) and adapt it for Armenia. We estimate that the costs of compliance with EU standards as a percentage of production costs will fall for Armenian exports to the EU of agricultural (manufacturing) products from 15.8 (21.6) percent of production to 11.8 (16.2) percent of production. But there are costs of facilities to meet EU standards and in development of the National Quality Infrastructure that we estimate will diminish the cost reduction by about 2 percent. Since the CIS market is predominantly regulated by ―GOST‖ regulations, we do not assume that production costs for Armenian exporters would fall on exports to any market other than the EU. Details are in Appendix E.

An important policy caveat to the above paragraph is that we do not recommend that Armenia adopt all
under the coefficient in parentheses. Sharpened q-values that correct p-values for the false discovery rate (FDR) are in square brackets. A sharpened q-value of 1.00 indicates the null hypothesis is not rejected at any level of FDR.

(4) Outcomes: Gender Equitable Index is a standardized index of 14 gender-progressive statements the respondent agrees with (index standardized as per method from Kling et al. 2007). Higher scores indicate more gender-equitable attitudes. The education sub-index, women's role sub-index, and masculinity norms sub-index are constructed from (mutually exclusive) statements in the Gender Equitable Index.

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