rafmacalaba/datause-encoder-data huggingface dataset:
than 153.7 times. The bottom row shows the ratio between the two series. The whole difference emerged during the 1970s. It increased further to 1981 but was almost reversed to the 1979-level by 1984. Comparison between the SII series for mining & quarrying and the SIS series for mining shows an even wider disparity. Also here the disparity grew up during the 1970s. It reached a maximum in 1979 but was later reversed somewhat. From 1960 to 1984 the SIS series for mining ir.creased 220.9 times against 137.9 times for the SII series for mining & quarrying. These are the differences we need to explain. We begin with the series for manufacturing.

The SII series include all persons insured under the Social Insurance Law No. 506, unchanged in force since March 1, 1965. The law entitlcs all persons who are employed by a contract of service to benefits from social insurance with contributions from employee and/or employer. The information about wages is collected in the context of contribution payments: "The said wages have been calculated from the amount subject to insurance deductions" (Statistical Yearbook of Turkey, 1987, p.177). The following categories are not covered by the law: agricultural workers, self-employed, and persons (mainly government employees) contributing to anyone of the public pension funds established by law. Before the 1965 law, social insurance covered employees in establishments with 10 employees and more and from 1952 also in establishments with 4-9 employees in cities with more than 50,000

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# **On the Impact of Demographic Change on Growth, Savings, and Poverty**

Marcio Cruz<sup>1</sup> and S. Amer Ahmed<sup>2</sup> Keywords: Demographic change, economic growth, savings, poverty.

JEL: J11, O11, O47, I32.

Acknowledgements: The analysis for this paper was originally produced as background material for the World Bank Group’s Global Monitoring Report 2015/2016: Development Goals in an Era of Demographic Change. The authors are grateful to Ayhan Kose, Philip Schellekens, Bryce Quillin, Michele Gragnolati, Ronald Lee, Andy Mason, Robert Schmidt, and the reviewers of the Global Monitoring Report 2015/2016 for inputs and comments. Pinyi Chen, Huade Huo, and Linghui Zhu provided valuable research assistance. This paper was supported by the Knowledge for Change Partnership multi-donor trust fund.

> 1 The World Bank and Federal University of Paraná. Email: marciocruz@worldbank.org .

> 2 The World Bank. Email: sahmed20@worldbank.org.

1
political participation , economic participation and decision making, and power over economic resources.

In a similar spirit to the HDI, the _<u>Multidimensional Poverty Index</u>_ (MPI) was developed by Alkire and Santos (2010a), in work done for the 2010 HDR. The authors choose 10 components for the MPI; two for health (malnutrition, and child mortality), two for education (years of schooling and school enrolment), and six aim to capture ―living standards‖ (including both access to services and proxies for household wealth). Poverty is measured separately in each of these 10 dimensions, each with its own weight. In keeping with the HDI, the three main headings—health, education, and living standards—are weighted equally (one-third each) to form the composite index. A household is identified as being poor if it is deprived across at least 30% of the weighted indicators. While the HDI uses aggregate country-level data, the MPI uses household-level data, which is then aggregated to the country level. Alkire and Santos construct their MPI for more than 100 countries.<sup>4</sup> Mashups have been devised for other dimensions of development. The ― _<u>Economic Freedom of the World Index</u>_ ‖ is a composite of indices of the size of government, property rights, monetary measures (including the inflation rate and freedom to hold foreign currency accounts), trade openness and regulation of finance, labor and business (Gwartney and Lawson, 2009). The ― _<u>Worldwide Governance Indicators</u>_ ‖ (WGI) (Kaufmann, Kraay and Mastruzzi, 2009), is a set of mashup indices, one for each of six assumed dimensions of governance: voice and accountability, political stability and lack of violence or terrorism, governmental effectiveness, regulatory quality, rule of law, and corruption. The WGI covers some 200 countries and is now available for multiple years.

Probably the most well-known mashup index produced by the World Bank Group is the ― _<u>Ease of Doing Business Index</u>_ ‖—hereafter the Doing Business Index (DBI).<sup>5</sup> This is a simple average of country rankings for ten indices aiming to measure how easy it is to open and close a business, get construction permits, hire workers, register property, get credit, pay taxes, trade across borders and enforce contracts. Unlike most of the mashup indices, DBI collects its own

> 4   See Ravallion (2010a) for further discussion of multidimensional indices of poverty, including the MPI.

> 5   This developed from an original data compilation documented in Djankov et al. (2002).

5
There is a conspicuous lack of studies in other regions in the Himalaya. A Department for International Development (DFID) systematic review (Knox et al., 2011) on projected impacts of climate change on food productivity on Africa and South Asia reports a single article for both Bhutan and Nepal. While the report indicates India is widely studied (15 sources), most of them exclude the Indian Himalayas (Knox et al., 2011). Similarly, although literature on future impact of climate change on agriculture in China is increasing, the Xizang province is either aggregated with other regions (You et al., 2009) or skipped (Tao et al., 2008).

# **4.3 Impacts on human health**

Published information on possible health consequences of climate change in mountain regions is extremely limited. Most available literature on future projection is speculative. Many sources (e.g. Ebi et al., 2007; ICIMOD, 2010) suggest the likelihood of spreading vector-borne pathogens as new habitats become available at altitudes that were formerly unsuitable, increasing prevalence of diarrheal diseases with changes in freshwater quality and availability, and increased weather related impacts and food security concerns.

McMichael et al. (2004) find through a regional human health impact assessment using Hadley Center GCM projections that climate change induced risk on health outcomes will more than double by the year 2030 in some regions. The study found that in Bhutan, Nepal and India (region SEAR-D), the risks to human health due to flooding and malnutrition would be highest, followed by a modest increase in diarrhea and malaria (McMichael et al., 2004). For China (region WPR-B), flood and malaria related risks were projected to increase, and diarrhea and malnutrition risks are projected to increase and decrease, respectively, but nominally (McMichael et al., 2004). However, future projections of health impacts due to climate change are uncertain (Kolstad and Johansson, 2011; McMichael, 2006; Patz et al., 2005).

Few studies have modeled vector-borne disease expansion under climate change in the Himalayas, and those available are mostly limited to malaria (Sarkar, 2011). Bhattacharya et al. (2006) project country-wide malaria expansion in India under HadRM2 projections of daily temperature and relative humidity run under the IS92a emission scenario. They considered three different classes of temperature transmission windows (between 15-35°C) for two malaria parasite species and a transmission window of relative humidity (55-90%), and found that Himalayan states will become malaria prone in the 2050s.

18
BOTSWANA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 5. Trading across borders is costly for Botswana**

<!-- Start of picture text --> a. Export costs b. Import costs<br>CAR CAR<br>Niger Zimbabwe<br>DRC Rwanda<br>Zimbabwe DRC<br>Rwanda Niger<br>Botswana 3.01 Botswana 3.39<br>Zambia Zambia<br>Sudan Sudan<br>SSA 1.96 SSA 2.49<br>South Africa South Africa<br>Tanzania Tanzania<br>OECD OECD<br>0 1 2 3 4 5 6 0 1 2 3 4 5 6<br>US$ thousand per container, year 2010 US$ thousand per container, year 2010<br><!-- End of picture text --> _Source:_ Doing Business Database 2010.

_Note:_ CAR = Central African Republic; SSA = Sub-Saharan Africa; OECD = Organisation for Economic Co-operation and Development; DRC = Democratic Republic of Congo.

# Roads

## **Achievements**

Overall, Botswana has made remarkable achievements in its road sector, expanding its total paved road coverage from just 12 km at independence in 1966 to about 7,892 km in 2008. Moreover Botswana has tackled the maintenance of its existing roads comparably well, even though a separate road fund has not yet been established.

The total road network length was 28,152 km as of 2008. The classified network, with about 17,735 km, consists of primary and secondary networks (about 5,000 km) and 12,700 km of tertiary roads. The public highway network, which falls under the jurisdiction of the roads department, consists of 6,116 km of paved and 2,800 km of unpaved road. District roads fall under the responsibility of the district councils. The unclassified network is estimated to be around 5,668 km and the urban network 4,749 km.

Botswana has a strong record of road network expansion, quality, andto much lesser extent maintenance. The quality of roads is comparable to that found in the African MICs and only second to South Africa and Mauritius (figure 6). Eighty percent of the main road network and (perhaps even more impressive) 73 percent of the rural network are in good or fair condition (table 2). Other indicators, such as network density and road traffic, are below the typical levels for low-income countries (LICs). The low network density is correlated to the desert geography of much of the country rather than to a lack of connectivity.

10
# <u>Table 10:  Ex-post Matching of Trade Credit Terms</u>

The reported estimates are from multinomial logit regressions (AR_gap=0 is the base outcome). AR_gap is a dummy variable equal to 1 if the difference between the number of days offered to the firm by its suppliers less the number of days until the firm pays its suppliers is greater than zero (i.e. the firm prepays its payables); equal to 0 if the difference equals zero (i.e. the firm pays its suppliers on time); and equal to - 1 if the difference is less than zero (i.e. the firms pays its suppliers late). See Table 1 for variable definitions. All regressions include 12 sector dummies and 17 city dummies. Robust p-values are shown in parentheses, ***, **, * indicate significance at the 1%, 5%, and 10% level, respectively.

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|||**= – 1**|_AP_|__gap_|**= + 1**||
|L_Age<br>L_emp<br>D_Foreign|-0.05<br>[0.82]<br>0.14<br>[0.19]<br>0.29<br>[0.50]|-0.22<br>[0.41]<br>0.22<br>[0.10]<br>-0.23<br>[0.68]|-0.23<br>[0.29]<br>0.11<br>[0.31]<br>0.41<br>[0.37]|-0.41<br>[0.03]**<br>0.13<br>[0.17]<br>-0.23<br>[0.58]|-0.62<br>[0.02]**<br>0.24<br>[0.06]*<br>0.05<br>[0.93]|-0.45<br>[0.02]**<br>0.13<br>[0.16]<br>-0.39<br>[0.37]|
|D_State<br>D_Export|-0.13<br>[0.73]<br>0.19<br>[0.70]|-0.52<br>[0.27]<br>0.27<br>[0.65]|0.02<br>[0.97]<br>0.06<br>[0.91]|-0.39<br>[0.27]<br>-0.60<br>[0.20]|-1.11<br>[0.02]**<br>-1.59<br>[0.01]***|-0.34<br>[0.34]<br>-0.72<br>[0.13]|
|D1_AR_gap<br>D2_AR_gap<br>LC_unused<br>D_RE<br>AP_discount|-0.23<br>[0.55]<br>-0.89<br>[0.03]**<br>0.25<br>[0.67]|-0.12<br>[0.81]<br>-1.28<br>[0.02]**<br>-0.38<br>[0.31]|-0.23<br>[0.55]<br>-1.40<br>[0.00]***<br>0.40<br>[0.46]|0.56<br>[0.06]*<br>-0.48<br>[0.17]<br>-0.41<br>[0.45]|0.99<br>[0.01]**<br>-0.50<br>[0.29]<br>-0.56<br>[0.12]|0.41<br>[0.18]<br>-0.62<br>[0.08]*<br>0.89<br>[0.04]**|
|Constant|-22.32<br>[0.00]***|-0.43<br>[0.80]|-0.87<br>[0.55]|0.30<br>[0.79]|0.94<br>[0.58]|-0.91<br>[0.56]|
|Observations|583|374|569|583|374|569|
|PseudoR-squared|0.23|0.28|0.23|0.23|0.28|0.23|

47
16 Thus, using an instrumental variables<sup>technique in the estimation of equation</sup> (9), with a, as an instrument of di,17 allows<sup>us to obtain a consistent estimate</sup> of 34, the coefficient of the<sup>effectiveness of the program.18</sup>

# 4. Overview of the Data

All the data used in our analysis<sup>are taken from the World Bank's ANDREX</sup> data b'Ase except the real exchange<sup>rate, which comes from IMF calculations. We</sup> consider a sample that contains<sup>observations from 77 developing countries during</sup> the 1970-88 sample period. They<sup>are ones for which data are available for all</sup> relevant macroeconomic variables<sup>for the period 1970-88. We work with the data</sup> in current and constant prices.<sup>Because most EIAL countries carried out a real</sup> depreciation in 1985-88, the<sup>relative price of investmen! goods and exports rose</sup> relative to the early 1980s.<sup>Therefore, to measure the contribution of growth</sup> in the supply response of exports,<sup>it is better to work with the investment to</sup> GDP and export to GDP ratios<sup>in constant prices. For completeness and to satisfy</sup> the adding up condition we also<sup>work with saving ratios at constant prices. For</sup> purposes of this analysis, the<sup>countries were grouped into two categories:</sup> EIALs, the program countries<sup>and a "control" group--the non-program countries-</sup> -consisting of OALs and NALs.<sup>The OAL's are considered non-program countries</sup> because they have received too<sup>few adjustment loans during the period analysed.</sup> The sample period was, as noted,<sup>divided into three periods: 1970-80</sup> (first), 1981-84 (second) and 1985-88<sup>(third), with the latter corresponding to</sup> the adjustment period. We compare performance in this<sup>third period in the</sup> program countries with respect<sup>some counterfactual scenario of what would have</sup> 17 This solution to the sclectivity-bias<sup>problem of an instrumental</sup> variable method means that we<sup>do not have toassume</sup> that the error terms of equations (4) and (5) are uncorrelated.

18 The instrumental variable<sup>method used here is more efficient than the</sup> two-stage least squares method suggested<sup>by Barnow, Cain and Goldberger (1981),</sup> principally because the robustness<sup>of the two-stage method depends on the well</sup> specification of the status equation<sup>and on the distribution assumption madein</sup> the estimation of equation (10) more<sup>than the instrumental variable procedure</sup> does. In addition, with the instrumental<sup>variable estimation,</sup><sup>**we**do not need</sup> to correct the standard errors obtained<sup>from equation (9), while the standard</sup> errors o'4tained from a two-stage least<sup>squares estimation must be corrected.</sup> Because di has been estimated, the<sup>standard errors underestimate the true</sup> standard errors.
|Table 4 Resource allocation||
|---|---|
|Dependent: Change in PCR|PTR Per pupil teacher salary|
|Sample: Non-city divisions||
|Total test score in 2005 0.0126|0.0058 -0.2789|
|(1.28)|(0.59) (0.66)|
|PCR 2005 -0.4843|0.2120 -4.7227|
|(10.99)|(6.02) (8.00)|
|PTR 2005 -0.0383|-0.9420 0.9491|
|(1.56)|(28.54) (2.29)|
|Per pupil teacher salary 2005 -0.0132|-0.0282 -0.0588|
|(4.10)|(8.85) (0.95)|
|Division fixed effects yes|yes yes|
|Number of observations 4025|4027 4027|
|Number of divisions 86|86 86|
|R squared (within) 0.2913|0.5830 0.0442|
|Dependent: Change in PCR|PTR Per pupil teacher salary|
|Sample: City divisions||
|Total test score in 2005 0.0227|-0.0141 0.3016|
|(3.81)|(1.59) (1.74)|
|PCR 2005 -0.4762|0.2401 -3.0243|
|(13.98)|(6.34) (6.40)|
|PTR 2005 0.0123|-0.8433 0.8563|
|(0.74)|(22.20) (2.48)|
|Per pupil teacher salary 2005 -0.0079|-0.0220 0.1351|
|(4.93)|(5.88) (1.81)|
|Division fixed effects yes|yes yes|
|Number of observations 16357|16267 16267|
|Number of divisions 60|60 60|
|R squared (within) 0.1947|0.4577 0.0337|

Numbers in parentheses are absolute t values using Huber robust standard errors with division clusters.

14
expansion at the primary level created a severe bottleneck at the secondary level, and the girls lost out facing erce competition for a limited number of secondary school slots. The evidence suggests that the boys crowded out the girls at the senior secondary level, and the crowding out was experienced by girls irrespective of their father's schooling level. Contrasting evidence from the matrilineal island West Sumatra and the other patrilineal islands favors the hypothesis that the primary mechanism behind the gender-based crowing out is social norms against girls in a patrilineal society. The insight regarding the unintended bottleneck and its perverse distributional consequences are of general relevance; any public policy that expands opportunity at a given level may end up unleashing economic forces that crowd out weaker social groups at the next level. To deal with such adverse crowding out, it is necessary to implement complementary policies targeting the social groups at risk of such crowding out.

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30
**Figure 1: World Ethanol Production**

<!-- Start of picture text --> 80<br>US Brazil EU China India Canada Other<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>2004 2005 2006 2007 2008<br><!-- End of picture text --> Source: REN21 (2005; 2006; 2008; 2009), Renewable Fuels Association (2008) Table 1 presents biofuels production by country for the 2004-2008 period. In 2006, the US surpassed Brazil, the longtime leader, to become the leading fuel ethanol producer in the world by producing over 18 billion liters (20% more than the previous year) (REN21, 2008). Aside from the US and Brazil, significant production increases are found in France, China and Canada in recent years. Australia, Germany, Spain Colombia, India, Jamaica, Malawi, Poland, South Africa, Sweden, Thailand, and Zambia also engage in the commercial production of ethanol.

6
8 (3)

# vOMI) = **pO(t) I,o(t 2)**

where p0(t) is the time 0 price of a default-free zero coupon bond that pays $1 at time t. Similarly, one can value a discount bond's promised floating rate interest payment to be paid at time t. Let D denote the discount bond's level of principal and let s be the spread over the yield on a default-free six month bond issued six months prior to the interest payment date (e.g., the spread of 13/16% over LIBOR). Then the value of this discount bond interest payment equals<sup>6</sup>

# (4)

**VI (D,t)** = [e<sup>0</sup> 2po(t -.5) - po(t)] D 0(t2 )

These valuation formulas imply that the variability of the prices of the bonds will be less than that of the underlying state variable; for an enhanced bond, both its rolling guarantee as well as its limited liability will lead to the bond's price being a concave function of the state variable. This is illustrated in Figure 1 which plots the theoretical price of Mexico par and discount bonds as a function of the state variable, z.'

# 2.3 Estimation Techniques

Formulas (3) and (4) require default-free, zero-coupon bond prices, po(t), maturing at each coupon payment date. We constructed this term structure of bond prices using data on Treasury bill, Treasury strip, and LIBOR rates to estimate the parameters of the Vasicek (1977) bond pricing model. This model then allowed us to find values of po(t) for any time until

## 6See Borensztein and Pennacchi (1990) for details in valuing floating rate payments.

These bond prices represent interest rates and times until maturity that existed on March 24, 1990, the beginning of our sample period. A value of eu/u = -.222725 was assumed, which is the value we later obtain using our estimation procedure. The estimates for the state variables z (see below) varied between 1 and 3. Note that the discount bond can be valued above 100 for large values of z because of the spread, s, on the bond.
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