rafmacalaba/datause-encoder-data huggingface dataset:
**Table A1.4: Data on debt betas**

|**Company**|**Bond**|**Premium (basis points)**|**Debt betaa**|
|---|---|---|---|
|BAA|£250m, 8.5%, 2021|85|0.14|
|Electrobras|$50m, 10%, 1999|431|0.72|
|Telebras|Lira 350 billion, 13%, 1999|345|0.58|
|BT|£500m, 7.125%, 2003|16|0.03|
|BT|£229m, 12.25%, 2006|35|0.06|
|Nynex|$150m, 8.75%, 2004|76|0.13|

_Note:_ **a** A simplification has been made by assuming that the equity risk premium is constant across countries at 6%.

_Sources: Financial Times_ and Datastream.

As can be seen from the table, debt betas can vary from close to zero, for large utility companies in developed countries, to close to one, for utilities in developing countries. Of course, the assumptions underlying the debt beta calculation have an important impact on the value found and so these figures are being used purely for illustrative purposes rather than precise measures of the actual debt betas. Table A1.5 provides an estimate of the asset beta for two of the companies with debt betas. These are then compared to the asset betas calculated on the standard assumption.

**Table** A1.5: Asset beta estimates

|**Company**|**Equity beta**|**Debt beta**|**Gearing**|**Asset beta**|
|---|---|---|---|---|
|BT|0.98|0.06|0.12|0.87|
|Nynex|0.69|0.13|0.35|0.49|

**Table** A1.6: **Impact on asset beta estimate**

|**Company**|**Zero debt beta**|**Non-zero debt beta**|
|---|---|---|
|BT|0.87|0.87|
|Nynex|0.45|0.49|

43
the data used in estimation. Section 6 discusses parameter estimates, provides evidence on within-sample model fit, and compares the short-run impacts predicted by the model with the available post-reform evidence. Section 7 uses the model to analyze how different policy features, included or not in the reform, impact gender equity and household decisions. Section 8 concludes.

# **2 Related literature**

This study builds on a literature, pioneered by Fields and Mitchell (1984) and Mitchell and Fields (1984) that models how the structure of earnings, social security, and pension benefits affect retirement behavior. Their model assumed that individuals make a one time retirement age decision, taking into account future expected earnings and retirement benefits. They found that wealthier individuals retire earlier, and those who expect to gain more by postponing retirement retire later. Subsequent research developed and implemented fully dynamic modeling approaches that incorporate uncertainty in decision-making over time. An early dynamic model by Gustman and Steinmeier (1986) showed how pension benefits affect the lifetime budget constraint and alter the price of leisure at different ages, thus influencing the choice of retirement age. Stock and Wise (1990) analyzed the effect of pension plan provisions on the retirement age and also emphasized the importance of modeling uncertainty in accurately capturing the option value embedded in the decision to retire. Their estimation was based on a retirement decision rule that was motivated by a dynamic programming rule, but was computationally less complex. Berkovec and Stern (1991) estimated the first dynamic discrete choice model of individual retirement decisions using a dynamic programming set-up. Subsequent papers additionally incorporated into the basic dynamic discrete choice modeling framework other aspects, such as health expenditure risk, savings and detailed institutional pension rules to provide a fuller accounting of retirement determinants.<sup>6</sup> The more recent literature estimates dynamic structural models of the joint retirement decisions of husbands and wives. Our modeling framework is most closely related to that of 6See, e.g., Rust and Phelan (1997), French (2005), Blau and Gilleskie (2008), French and Jones (2011) and Blundell et al. (2016) for a survey.

4
**Table A.4 Proportion of Individuals Staying in School by Grade and Primary School Type**

||Six Year Primary|Schools|Five Year Primary|Schools|
|---|---|---|---|---|
|Grade|Proportion|N|Proportion|N|
|2|1.00|1310|1.00|4193|
|3|1.00|1310|0.99|4186|
|4|1.00|1296|0.99|4118|
|5|0.99|1285|0.98|4019|
|6|0.98|1257|0.91|3904|
|7|0.95|1211|0.95|3484|
|8|0.92|1122|0.87|3238|
|9|0.90|1011|0.43|2712|
|10|0.47|877|0.68|1134|
|11|0.95|388|0.84|729|
|12|0.91|351|0.62|574|
|13|0.36|305|0.59|329|
|14|0.91|100|0.81|183|
|15|0.83|83|0.58|138|
|16|0.36|61|0.32|74|
|17|0.40|20|0.21|24|
|18|0.80|5|0.50|4|
|19|0.00|3|0.50|2|
|20|0.00|0|0.00|1|

Notes: Proportions are conditional on school enrollment the previous year. Assumes children start school at age 7 and do not skip. Source: RCRE Supplemental Survey (2004).

67
among the public, availability on a monthly basis, publication without long delays, and infrequent data revisions, unlike, say, GDP deflators). In almost all of these countries, consumer price indices are typically fixed-weight averages of prices of a "basket" of goods and services. Price data are gathered by statistical agencies through regular visits to specified sales outlets. The weights are based on the distribution by product of consumer expenditure and are revised at regular intervals as the composition of household expenditure changes. These indices are not designed to measure the effect of changes in the types, amounts and quality of the goods and services households purchase. Specifically, in measuring inflation, existing price indices suffer from three main sources of bias, whose relative importance is of course likely to vary across countries:

- _Substitution bias,_ which arises because consumers can be expected to switch expenditure away from more expensive products toward cheaper products, and away from more expensive stores toward cheaper ones. Regular reviews of the weights in the index and of the selection of outlets chosen for sampling purposes helps to reduce substitution bias, but the bias is likely to persist in the periods between these reviews.

- _Quality bias,_ which arises because the quality of goods and services within a particular category may change, but the change may not be fully reflected in computing the price index. If quality increases over time (as tends to be the case for many products), failure to account for it fully will lead to overstatement of inflation.

- _New products bias,_ which stems from the fact that new goods are continually introduced whereas existing ones are continuously witdrawn. To the extent that new products widen choice opportunities for households and allow them to achieve a given level of utility at a cost that may be lower than previously, failure to account for them will lead to an overestimate of inflation.

These sources of bias can be quite significant in practice. For instance, the Boskin Report (see Boskin et al. (1996)) estimated that, in the United States, substitution bias (including outlet substitution bias) was about 0.5 percent a year in terms of CPI inflation whereas quality and new products bias represented about 0.6 percent per annum in 1995-96. In general, however, accounting for bias can be very difficult. One reason is that estimates 29
# **RECENT COHORTS**

A cross-sectional sample may not be very instructive in a rapidly changing economy. For this reason the sample is divided into two groups: recent labor market entrants and workers with more than six years experience. Focusing on recent graduates allows one to reflect the fact that reforms may not have affected all workers by 1993. Younger workers are expected to be more affected by the recent changes as they enter directly into a free market wage economy. In fact, those with less years of labor market experience receive higher returns to schooling: 14 percent versus 4 percent for more experienced workers (Table 9). In China, too, the returns to schooling for younger workers are higher than for more experienced workers, showing that in both Vietnam and China the returns to schooling increase as newer cohorts enter the labor market (World Bank 1996).

Table 9: Vietnam: Earnings Functions for Recent Labor Market Entrants and Experienced Workers

|Variable|Recent Entrants<br>(less than 6 years)|Experienced Workers<br>(more than 5 years)|
|---|---|---|
|Constant|0.217|0.792|
|Years of schooling|0.144<br>(5.9)|0.044<br>(7.5)|
|Years of experience|<br>0.204*<br>(1.3)|0.055<br>(6.5)|
|Experience-squared|0.006*<br>(0.2)|-0.001<br>(5.9)|
|Log hours-worked|0.489<br>(2.6)|0.759<br>(11.7)|
|R-squared|0.138|0.103|
|N|328|1,931|
|Source: VLSS 1992-93|||

Note: t-statistics appear in parentheses; all variables are significant at the 10% level or better, except where indicated by * 20
**Table 5 (continued)**

# **Parameter Estimates for Error Correction Model, Nairobi**

||**Mtwara**|**Musoma**|**Mwanza**|**Shinyanga **|**Singida**|**Songea**|**Sumbawanga **|**Tabora**|**Tanga **|
|---|---|---|---|---|---|---|---|---|---|
||-0.01|-0.00|-0.00|0.01|-0.02*|-0.09**|-0.05*|-0.03|-0.02|
|_μ_|(0.36)|(0.31)|(0.11)|(1.04)|(1.74)|(2.26)|(1.75)|(1.48)|(0.94)|
|(<br>E<br>−<br>i<br>)|0.30***|0.24***|0.20***|0.13***|0.14**|0.16**|0.14***|0.15***|0.15***|
|pt−1<br>pt−1<br>|(3.72)|(3.69)|(4.06)|(3.37)|(3.01)|(2.52)|(3.02)|(2.88)|(2.98)|
|Δ<br>E|-0.03|0.31***|0.22**|0.15**|0.14|0.14|0.16|0.26**|0.36***|
|pt|(0.16)|(3.50)|(2.44)|(2.11)|(1.53)|(0.98)|(1.30)|(2.79)|(3.15)|
|Δ<br>F|-0.27|0.44**|0.49**|0.17|0.24|0.21|0.08|0.34|0.29|
|pt|(0.90)|(2.12)|(2.33)|(0.89)|(1.00)|(0.77)|(0.27)|(1.14)|(1.44)|
|Δ<br>I|-1.06|2.27*|2.21**|0.35|2.77***|1.03|1.03|2.74***|1.72|
|pt|(0.52)|(1.69)|(2.42)|(0.37)|(3.03)|(0.59)|(0.69)|(2.62)|(1.10)|
|I|-3.52|-4.58**|-3.65**|-4.19***|-3.03*|-0.52|-4.42*|-3.03|-3.32|
|BAN|(1.09)|(2.51)|(2.43)|(2.66)|(1.84)|(0.19)|(1.98)|(1.56)|(1.58)|
|C~~(~~<sup>2πt</sup><br>)|0.12***|-0.03*|0.03*|0.05***|0.04**|0.07**|0.04*|0.05**|0.08***|
|os<br>12<br>|(3.59)|(1.73)|(1.93)|(3.37)|(2.34)|(2.32)|(1.78)|(2.32)|(4.34)|
|Si<sup>2πt</sup><br>|-0.01|-0.02|-0.02**|0.02*|0.00|-0.03*|-0.06***|-0.04**|0.02|
|n~~(~~<br>12<br>)|(0.25)|(1.48)|(2.15)|(1.78)|(0.24)|(1.72)|(4.07)|(2.49)|(0.98)|
|NDVI|-0.56**|-0.22|-0.16|-0.01|-0.23***|-1.06***|-0.61*|-0.16*|-0.71***|
|t|(2.16)|(1.14)|(1.36)|(-0.06)|(3.12)|(-2.75)|(1.94)|(1.80)|(3.30)|
|_R-square_|0.26|0.22|0.28|0.25|0.28|0.20|0.23|0.26|0.34|

**Notes:** Each regression has 144 monthly observations. The dependent variable is the change in the logarithm of the nominal price in market _i_ . Absolute (robust) t- statistics in parentheses, significance level, * = 10 percent, ** = 5 percent, *** = 1 percent.
The growth in labor productivity that accompanied the structural transformation of Uganda’s economy in the 2000s thus must have had sources that were far more deep seated than the internal economies of scale and life cycle effects that the transformation also entailed. The authors investigate some of these other sources in a companion paper drawing on the data being analyzed here.

# **_1.8. Structure of the paper_**

The rest of the paper is organized as follows. We describe our date in section 2, where we also provide details of the various dimensions of the structural change that Uganda’s economy underwent between the 2001 and 2010 waves of the UBR by breaking down the aggregate shift in employment between the traded and non‐traded sectors, first, into flows across narrower industry categories, and, in the case of the traded sector, between comparative advantage industries and others and, secondly, into shifts across the size and age distributions firms within each sector and industry. Section 3 analyses the linkage between labor productivity and sectorial and inter‐industry shifts in employment and the roles that employment shifts across the size and age distributions of employers play in that relationship. Section 4 concludes.

# **2. Inter‐sectorial shifts in employment in the 2000s: UBR 2001 and UBR 2010**

# **_2.1. Sources of data:  The UBR and the UBI data sets_**

# 2.1.1. The Uganda Business Register (UBR):  The 2001 and 2010 Waves

We need data on two sets of variables in order to address the hierarchy of questions we just outlined about structural transformation in the Ugandan economy. First we need data on measures of changes over an extended period in the distribution of employment across major industries. Secondly, we need measures of gaps in labor productivity between those industries over the period of interest in order to determine whether or not the observed transformation has been productivity enhancing and, if it has been, to identify the specific sources of the productivity gains.<sup>8</sup>

> 8 Only two possible potential sources of productivity gains are considered in this paper. Other sources are taken up in the companion paper we have already alluded to.

9
The results show that geographic targeting with any of the indicators is a significant improvement over an untargeted regime in which resources are distributed equally across all districts. There is, therefore, substantial potential for geographic targeting in Peru. However, targeting outcomes are quite similar for all of the targeting indicators I consider, and differences in outcomes between indicators are not significant at conventional levels of significance.

The rest of the paper proceeds as follows. Section 2 briefly describes alternative geographic targeting indicators available in Peru. Section 3 describes the methodology I use to compare indicators. It also describes altemative performance measures, identifies formulas that can be used to allocate resources to districts, describes data requirements, and states the assumptions made for the simulation exercise. Section 4 summarizes the results of the analysis. Section 5 draws conclusions.

# **2. Geographic targeting indicators in Peru**

In 1997 there were 13 administrative regions, 24 departments, 194 provinces, and 1812 districts in Peru (Webb and Fernandez Baca 1997, p. 112). Recent discussion about the geographic targeting of government programs has focused on the use of district-level averages. Districts can be quite small in Peru: According to the 1993 Population and Housing Census, the average district population was about 12,600 inhabitants, but some, predominantly rural districts had less than 200 inhabitants.

# _Infant mortality_

The infant mortality rate measures the fraction of children ever born who do not reach the first year of age. In Peru, as in many other developing countries, one would be loathe to estimate infant mortality on the basis of seriously incomplete death registries. To address this problem, demographers have developed indirect methods to estimate mortality at early ages (Hill, Zlotnik and Trussell 1983; Brass and Macrae 1984 and 1985; Trussell and Menken 1984). The Peruvian National Statistical Institute (INEI) used one such method--the preceding births technique--to estimate infant mortality in Peru.

The preceding births technique requires women to report the total number of children ever born and surviving at two different points in time. This information was available in Peru from the 1981 and 1993 population censuses. However, because the population of many districts is very small, applying the preceding births technique to individual districts would have produced highly uncertain results. INEI therefore followed a two-stage procedure. First, it estimated the infant mortality rate in every department, where the sample size was large enough. In addition, INEI regressed its estimate of the infant mortality rate in every department on some of its 3
Trade and Logistics in East Asia: A Development Agenda 2003 etc). In addition, The World Bank has continuously supported ongoing regional efforts by other development partners e.g. Cooperation with ADB in the Greater Mekong Sub-region (also supported by UNESCAP). Some country specific efforts directed at TF include the Philippines Logistics Study, Logistics Development and Trade Facilitation in Lao etc. The Mekong transport infrastructure development project in Vietnam for instance aims to improve the supply chain from point of production to point of export/import<sup>13</sup> .

UNESCAP<sup>14</sup> has been providing support to regional TF through its collaboration with UNECE in forming a Working Group on Transport and Border Crossing Facilitation. They have also collaborated to develop Euro-Asian linkages in 17 countries by identifying corridors and investing in infrastructure. The Shanghai Cooperation Organization (SCO) established in 2004 with 6 members includes 11 agreements and deals with different aspects of TF. TF will also feature in the next five-year development aid plan (2006-11) of the EU.

Thus, stronger engagement with member countries, ASEAN, UNESCAP, ADB and other development partners can further develop and implement regional strategies, and provide further impetus for TF.

# **5. Unilateral Approach to Trade Facilitation**

At the national level, the process of reform has to be triggered by the country itself, by garnering support from the political and bureaucratic echelons, as well as the trading community. The customs reform agenda must be included as part of a broader TF plan. At the same time, keeping abreast of developments in the regional and international arena is necessary. Setting up the infrastructure for TF is a difficult process for developing countries given their constraints on resources and expertise. Efforts by several countries are noteworthy in this regard (See Box 2).

> 13 The Trade and Transport Facilitation in South-East Europe Program <u>(TTFSE) is another example of an</u> endeavor bringing together eight countries, the World Bank, and donors with the aim of fostering trade by promoting more efficient and less costly trade flows across the region.

> 14 <u>Barry Cable</u> has outlined UNESCAP initiatives.

10
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