rafmacalaba/datause-encoder-data huggingface dataset:
> <sup>- 30 -</sup>

guidance exists as to how the register must be kept, monitored and approved by the company's auditor. Moreover, the supervisor will occasionally check that all regulations pertaining to the register are met.

The register is the Danish way of implementing the so-called winding-up directive from the European Commission. It serves to ensure that there are assets to cover technical provisions in case of a winding up procedure, if a company faces severe solvency problems. The registered assets in this case serve to fulfill obligations toward the policyholders, i.e., before liabilities toward any creditors are met.

# **D.   Valuation Rules - Assets**

The Danish accounting rules for life insurance and pension institutions are often termed market-value accounting rules. On the asset side the fair value model reflects the fact that all assets must be measured at their market value by using observable market prices or by using a valuation technique in the case of non-active markets. For the majority of assets - like shares and bonds - there is an active market where the assets are traded, giving rise to observable market prices. For property an economic model is used to provide an estimate of the market price. For the remaining assets - like unlisted shares - a best estimate for the market price must be applied.

One can always discuss whether the price information from financial markets truly reflect market values and how ownership issues - like pricing dominating ownership - should be dealt with in a market-value environment. This gives rise to the ongoing controversy about the distinction between market and fair values; however, this is a separate issue beyond the scope of this paper. In the following the terms, market value and fair value, are used interchangeably.

The road towards market valuation on assets was completed in 2002. In 1995 life insurance companies had been given the option to measure property at market value, and in 1998 it was made mandatory to value shares and property at fair values. Bonds which made up a large part of assets in most pension institutions had to be measured at "mathematical values", where the market price was increased until maturity assuming a constant interest rate as of the day of purchase. In 2002 it became mandatory to also measure bonds at their current market price.

Hence, since 2002 all assets in life insurance companies and pension funds have been measured at fair value with value changes - realized and unrealized - shown in the profit and loss account.

# **E.   The Prevalence of With-Profits Contracts**

Before discussing the evolving rules on the valuation of liabilities, two important features of the Danish scene need to be noted. These are the prevalence of with-profits contracts and the use of the so-called contribution principle.
For empirical analysis involving NTB inventories several indices have been used. One such measure is a frequency index (P ) showing the percentage of transactions (i.e. imports of a tariff line product from a given country) covered by some preselected group of nontariff measures, where Ni is transaction i, Di is a dummy variable that takes a value of ur.Lty if one or more NTB is applied to the transaction (or zero otherwise), and Nt is the total number of transactions in the product group. The first summation in equation (1) is done over all tariff line items while the second is over all countries exporting to j. 8/ Given that matched trade data are available, in which individual countries of origin for shipments are identified, a second index showing the share of total imports subject to NTMs can be computed. This trade coverage measure (C-) is defined as, (2) Cj 2 ((Di. tm x Vi,t-N)/.Vi,t-N where Vi,t-N represents the value of imports of item i in year (t-N) and Di.t-

- 8/ Walter utilized a different concept in his applications of equation (1). A four-digit SITC product (commodity) was considered to be "affected" by NTBs if one or more of the component tariff lines encounters a nontariff restriction. The reasoning here is that exporters of closely related products in the four-digit group arr normally aware of the NTB and modify their behavior to prevent the sp.A,d of the measure. Dinopoulos and Kreinin (1988) and Messerlin (1988) measure empirically the trade contraction that occurs on the part of exporters of similar products to those facing new restrictions and show that trade in these goods experienced major contractions. However, a shortcoming of the "affected" concept is that the magnitude of the trade response may vary considerably from product to product. While recent applications of equation (1) are normally made directly with tariff line data, we have utilized Walter's "affected" commodity concept in this study so all indices would be directly comparable. Direct trade coverage indices could not be computed for the 1960. since the required tariff line level trade data were not generally available.
form of banking regulation. Technology can help in other ways. In Uganda, over the past two years, Hewlett-Packard and others active in the microfinance industry have been working to increase the scale of microfinance. The team has developed, tested and is implementing a remote transaction system using handheld devices, which capture transaction data and use a GSM network to transmit this back to a head office server and, in turn, management information system. Hand-held tools are being used more generally by several micro-finance institutions to provide on the spot loan applications and approvals (see Microsave.org for other examples).

In Mexico, there have been innovative ways of trade finance using reverse factoring. The program developed by Nafin, a government development bank, allows many small suppliers to use their receivables from large credit-worthy buyers, including foreign MNCs, to receive working capital financing, effectively transferring their creditworthiness to allow small firms to access more and cheaper financing. What makes Nafin special is that it operates an internet-based platform, providing on-line services, reducing costs, increasing transparency and improving security. In the short-run, there is a subsidization of overhead costs, but by lowering costs for SME working capital, it expects to generate more business and become sustainable (see further Klapper, 2004).

Paulson and McAndrews (1998) provide a case study of how Standard Bank of South Africa tried a new ways of addressing an un-banked population. Already a decade ago, in 1993, Standard Bank set up E-Bank. It was a simple savings product offering card-only access, but supported by dedicated staff speaking a mix of relevant local languages and operating out of dedicated outlets to help overcome problems of illiteracy and concerns about security in a high crime environment. It had high start-up costs, but provided financial services to a low-income segment. Since then E-bank has been absorbed in the bank’s more general provision of financial services to low-income households.

These are examples of some specific market approaches and government interventions that can further enhance access. Many other examples like these exist. More generally, there is much emphasis recently on facilitating the mainstreaming of micro-finance 23
Reyes, J-D., M. Roberts, and L. C. Xu. 2017. “The Heterogeneous Growth Effects of the Business Environment: Firm-Level Evidence for a Global Sample of Cities.” Policy Research Working Paper 8114, World Bank, Washington, DC.

Sánchez-Martín, M. E., J. De Piniés, and K. Antoine. 2015 “Measuring the Determinants of Backward Linkages from FDI in Developing Economies: Is It a Matter of Size?” Policy Research Working Paper 7185, World Bank, Washington, DC.

Schreyer, P. (2000), ‘High-growth firms and employment,’ OECD Science, Technology and Industry Working Papers. No. 2000/03.

Törnqvist, Leo, Pentti Vartia, and Yrjö O. Vartia. 1985. “How Should Relative Changes Be Measured?” The American Statistician 39 (1): 43–46.

Wang, J., and M. Blomström. 1992. “Foreign Investment and Technology Transfer. A Simple Model.” European Economic Review 36 (1): 137–55.

- 26 -
expectancy to control for the intergenerational link, and use individual-level data on schooling attainment to examine the causal link. Finally, we undertake regional and economy-specific analyses to compare the effect of life-expectancy at birth on schooling across income regions and economies.

In 95% of the surveys, we find positive and statistically significant effects of life expectancy at birth on lifetime years of schooling. In only 2.3% of the surveys is the relationship negative and statistically significant. The effects are important economically. We find that an additional year of life expectancy at birth increases years of schooling for that birth cohort by 0.11 to 0.15 year. In addition, that one-year increase in life expectancy will increase the years of schooling of the children of that birth cohort by 0.025 year. Applying these estimates to changes in life expectancy since 1905, we conclude that rising life expectancy at birth accounts for about 70% of the rising years of schooling worldwide.

The next section reviews the recent studies that link life expectancy to schooling, followed by a section on the theoretical framework that guides our empirical approach. Section 4 describes the survey data we use and defines the variables. Sections 5 and 6 report the findings from our basic empirical model and from the extensions of that model, respectively. The final section compares our results with those of other studies and draws implications for policy.

# **2. Relevant Literature**

Recent studies have provided conflicting evidence regarding the Ben-Porath prediction that greater life expectancy increases human capital investment.<sup>1</sup> The principal challenge for these studies has been to establish causality, not just correlation. Studies have addressed this

> 1 Earlier studies, such as Kalemli-Ozcan, Ryder and Weil (2000), have been cited and reviewed thoroughly by recent studies included here (e.g., Bleakley, 2010b; Cutler et al., 2010; Cervellati and Sunde, 2009).  Here we focus on studies undertaken in the last decade.

3
dimensions, but without forming a single composite index. At the country level, the World Bank’s _Poverty Assessments_ and the _Poverty Reduction Strategy Papers_ of individual governments have typically drawn on multiple indicators (though naturally with varying emphasis), without forming a single composite index.<sup>4</sup> This paper argues that the real differences between the recent measures that are called “multidimensional” and standard approaches lie elsewhere. The first difference is in whether one believes that a single index of poverty could ever be a sufficient statistic, or whether multiple indices are required, each measuring different things using the best data available for that task— presenting us with a “large and eclectic dashboard” (Stiglitz et al., 2009, p.62). A second difference is also evident on closer inspection, namely how the analyst chooses to collapse multiple dimensions into one, recognizing that some degree of aggregation will probably be called for even in the “dashboard” approach.

In elaborating these two differences I will illustrate the arguments using the most welldeveloped and broadly applied MIP to date, namely that developed by Alkire and Santos (2010a), which is a special case of the class of measures proposed by Alkire and Foster (2007). The following section discusses the Alkire-Santos index, and whether it can be considered sufficient for measuring poverty and informing policy making. Section 3 turns to the issue of how one can go about aggregating across multiple dimensions when a degree of aggregation is called for to reduce the dimensionality. Section 4 concludes.

# **2. Can we measure poverty adequately with any single index?**

There are countless possibilities for forming composite indices by some form of essentially _ad hoc_ aggregation—giving what I term elsewhere “mashup indices” (Ravallion, 2010a). The issue in this section is not how this can be done but whether it is useful to do so. One can easily imagine situations in which one would not want a mashup index. Imagine you go for your annual medical checkup. Your doctor does all the usual tests, but tells you that she will base her assessment solely on a single composite index—rescaling and averaging all the test

> 4 The Bank’s Poverty Assessments (significant analytic reports covering virtually all developing countries) typically cover education, health and nutrition and access to infrastructure, in addition to income poverty.

3
captures the intent-to-treat impacts of the cash transfers. Standard errors are clustered at the randomization cluster level (as recommended by Abadie et al., 2017).<sup>19</sup> Intent-to-treat (ITT) estimates are unbiased but may underestimate the average treatment effects (ATE) of the cash transfers. The predicted beneficiary status imperfectly matches actual beneficiary status since 55% of predicted beneficiary households (based on the baseline PMT score) are actual beneficiaries. In order to estimate the average treatment effects on the treated, our preferred specification leverages the fact that we observe actual beneficiary status in the treatment villages, and that the selection rule was based on clearly established PMT variables (although measured with noise at baseline). We thus estimate a matched difference-indifferences specification (PSM-DID), in which actual beneficiary households in the treatment group are matched with households in the control group based on PMT variables from the baseline survey. Specifically, we implement propensity score matching (Caliendo and Kopeinig, 2008; Rosenbaum and Rubin, 1983) by estimating the propensity to participate to the program via a probit regression with variables from the PMT formula. Average treatment effects on the treated are then estimated via PSM: PP(XX) (2)<sup>PP</sup> PP(XX)|TT=1 where indicates treatment, ττ<sup>P</sup> = EE (EE[YY(1)|TT= 1, PP(XX)] −EE[YY(0)|TT= 0, PP(XX)]) is the outcome of interest for beneficiary households in the treatment group, and for matched households in the control group.<sup>20</sup> In the main TT= 1 YY(1) specification, we perform one-on-one matching of non-beneficiaries in the control group to YY(0) actual beneficiaries without replacement. We estimate the impact on the “first difference” outcome ( F , where 2015 is for follow-up survey and 2012 is for baseline survey) and cluster standard errors at the randomization cluster level, similar to the benchmark ,ii 2015,ii 2012,ii YY = YY −YY DID specification. We also conduct a series of robustness checks by adding different sets of controls and using alternative matching methods.<sup>21</sup>

> 19 For a limited number of outcomes of interest that are only available at follow-up, we employ a single difference OLS specification. While unbiased in case of random assignment (and since the treatment and control groups are balanced, see section 3.2), this approach is likely to be less precise than the DID specification when baseline information is available (McKenzie, 2012).

> 20 The PSM estimator is implemented in Stata as per Leuven and Sianesi (2014).

> 21 For variables that were not collected at baseline, we again run a simple-difference matching specification.

13
# **Supplementary Table S2b.**

|||Log of|||||Log of|||||Log of||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Export|||Exporter||Export|||Exporter||Export|||Exporter|
||Log of # of<br>Exporters|Value per<br>Exporter|Exporter<br>EntryRate|Exporter<br>Exit Rate|Turnover<br>Rate|Log of # of<br>Exporters|<br>Value per<br>Exporter|Exporter<br>EntryRate|Exporter<br>Exit Rate|Turnover<br>Rate|Log of # of<br>Exporters|<br>Value per<br>Exporter|<br>Exporter<br>EntryRate|<br> <br>Exporter<br>Exit Rate|Turnover<br>Rate|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|(13)|(14)|(15)|
|(Bank Credit / (Bank Credit + Stock))*Nunn|-52.365|49.074|18.610***|16.442***|34.812***|-108.901**|*<br>12.456|18.340***|17.500***|34.832***|-13.710|5.127|18.782**|* 15.631***|34.281***|
||(36.957)|(59.630)|(5.490)|(5.321)|(10.123)|(36.031)|(63.570)|(5.582)|(5.383)|(10.123)|(35.904)|(58.653)|(5.197)|(5.111)|(9.645)|
|(Bank Credit / (Bank Credit + Stock))*Nunn*Rule of law||||||154.406***|198.804**|1.998|-5.207|2.279||||||
|||||||(39.517)|(100.774)|(11.265)|(11.183)|(21.860)||||||
|Nunn*Rule of law||||||0.164<br>(0.245)|-0.919<br>(0.585)|-0.028<br>(0.068)|-0.000<br>(0.069)|-0.038<br>(0.134)||||||
|(Bank Credit / (Bank Credit + Stock))*Nunn*Strength of legal rights|||||||||||-40.219** <br>(20.227)|116.051**<br>(23.012)|* -8.283***<br>(2.689)|-5.869*** <br>(2.255)|-13.296***<br>(4.564)|
|Nunn*Strength of legal rights|||||||||||0.463***|-0.815***|0.041**|0.025*|0.065**|
||||||||||||(0.136)|(0.139)|(0.017)|(0.014)|(0.029)|
|(Bank Credit / (Bank Credit + Stock))*EXTFIN|38.736***|-27.820|-4.408*|-6.299**|-7.487*|37.600***|-28.187|-4.408*|-6.263**|-7.478*|39.060***|-29.570|-4.314*|-6.266**|-7.323*|
||(11.761)|(23.202)|(2.247)|(2.545)|(4.070)|(11.720)|(23.248)|(2.253)|(2.556)|(4.087)|(11.642)|(23.223)|(2.242)|(2.543)|(4.050)|
|_Controls:_||||||||||||||||
|(All Credit / GDP)*EXTFIN|0.031|0.283***|0.005|0.008|0.012|0.034|0.286***|0.005|0.008|0.012|0.031|0.284***|0.005|0.008|0.012|
||(0.034)|(0.084)|(0.008)|(0.009)|(0.015)|(0.034)|(0.084)|(0.008)|(0.009)|(0.015)|(0.034)|(0.085)|(0.008)|(0.009)|(0.015)|
|(All Credit / GDP)*Nunn|1.106***|0.752***|-0.067***|-0.059***|-0.130***|-0.036|0.728**|-0.066**|-0.065**|-0.119**|1.149***|0.611***|-0.052***|-0.039**|-0.093***|
||(0.104)|(0.174)|(0.016)|(0.017)|(0.031)|(0.200)|(0.325)|(0.029)|(0.032)|(0.058)|(0.119)|(0.214)|(0.019)|(0.018)|(0.034)|
|All Credit*Nunn*Rule of law||||||-0.178|-0.255|0.010|0.029|0.011||||||
|||||||(0.128)|(0.273)|(0.025)|(0.027)|(0.049)||||||
|All Credit*Nunn*Strength of legal rights|||||||||||-0.043|0.049|-0.003|-0.008|-0.014|
||||||||||||(0.046)|(0.073)|(0.006)|(0.006)|(0.012)|
|Observations|17,870|17,561|15,685|15,639|15,548|17,870|17,561|15,685|15,639|15,548|17,870|17,561|15,685|15,639|15,548|
|R-squared|0.885|0.551|0.531|0.500|0.554|0.889|0.551|0.531|0.500|0.554|0.887|0.553|0.533|0.502|0.556|
|Note: Robust standard errors, clustered at the country-year level, are in<br>*** p<0.01, ** p<0.05, * p<0.1|parentheses.|||||||||||||||
# **Appendix Table A.10 Determinants of High School Enrollment using Contemporaneous Growth in Nearest City  Instrument Conditional on Completing Middle School, 1991-2003**

||Depe|ndent Variable:|Enroll in High|School Next Ye|ar = 1|
|---|---|---|---|---|---|
|Model|1|2|3|4|5|
||IV-GMM|IV-GMM|IV-GMM|IV-GMM|IV-GMM|
|Share of Migrants in Village|-2.162***|-2.275***|-2.135**|-2.166***|-2.168***|
|Labor Force|(0.733)|(0.840)|(0.835)|(0.700)|(0.699)|
|Gender (1=male)|||0.026|0.030|0.029|
||||(0.022)|(0.022)|(0.022)|
|First Born (1=yes)|||0.047**|0.022|-0.011|
||||(0.023)|(0.023)|(0.033)|
|First Born in Household|||-0.062***|-0.072***|-0.056**|
|was Male (1=yes)|||(0.021)|(0.021)|(0.023)|
|Father's Years of Schooling||||0.020***<br>(0.005)|0.020***<br>(0.005)|
|Mother's Years of Schooling||||0.024***<br>(0.005)|0.023***<br>(0.005)|
|Number of Potential Migrants,|||||-0.040*|
|<br>Household, Male|||||(0.023)|
|Number of Potential Migrants,|||||-0.008|
|Household, Female|||||(0.019)|
|ln(Village Mean Income Per Capita)||0.006|0.006|0.016|0.019|
|||(0.093)|(0.091)|(0.090)|(0.091)|
|Village Total Land (/100)||0.001|0.001|0.001|0.001|
|||(0.002)|(0.002)|(0.002)|(0.002)|
|Village Cultivable Land Per Capita||0.316|0.272|0.223|0.209|
|Gini||(0.412)|(0.399)|(0.390)|(0.390)|
|(Village Labor Force)/10||-0.001|-0.001|-0.001|-0.001|
|||(0.001)|(0.001)|(0.001)|(0.001)|
|Cultivable Share of Village Land||0.152|0.147|0.091|0.095|
|||(0.175)|(0.169)|(0.165)|(0.166)|
|Cluster Corrected F statistic|7.636|6.626|6.647|6.846|6.840|
|Weak Instrument Robust Anderson-<br>Rubin F-Statistic|4.55|4.38|3.88|3.89|3.92|
|Anderson-Rubin p-value|0.033|0.036|0.049|0.049|0.048|
|Number of Obs.|2,372|2,372|2,372|2,372|2,372|

Notes: In parentheses, we show robust standard errors that allow for arbitrary correlation within city-year observations. All regressions control for factors related to village location with village fixed effects, and macroeconomic shocks using province*year fixed effects. All models are estimated using an instrumental variables-generalized method of moments estimator that is efficient in the presence of presence of heteroskedasticity and arbitrary within city-year cluster correlation (see Wooldridge 2002, page 193). Weak instrument robust Anderson-Rubin F statistics are calculated with a minimum distance estimator suggested by Finlay and Magnusson (2011). *-indicates significance at the 10 percent level; **- indicates significance at the 5 percent level; ***- indicates significance at the 1 percent level.

74
# **Longevity and Lifetime Education: Global Evidence from 919 Surveys**

Mohammad Mainul Hoque Bangladesh Institute of Development Studies Elizabeth M. King Brookings Institution Claudio E. Montenegro Department of Economics, University of Chile; The World Bank, and the German Development Institute.

Peter F. Orazem Department of Economics, Iowa State University

**Key Words:   life expectancy, lifetime education, cohort, human capital, Ben-Porath, identification**

**JEL: J10; I15; O15;**

**________________________** Corresponding author:  Peter F. Orazem, 267 Heady Hall, Department of Economics, Iowa State University, Ames, IA  50011-1070. Email:  pfo@iastate.edu  Phone:  (515) 294-8656/ FAX: (515) 294-0221. Responsibility for the contents of this article is entirely ours and should not be attributed to our affiliated institutions.