rafmacalaba/datause-encoder-data huggingface dataset:
family,<sup>21</sup> their incentive to do so declines post-reform since the amount of money at the father’s disposal to bequeath is smaller.

Testing the bargaining channel amounts to finding evidence that, within a particular type of family setup, women exposed to the reform have better autonomy outcomes. Examining the partition channel involves finding evidence that the following key patterns hold: (a) _A preference for nuclear households:_ Women in nuclear families should have higher autonomy than those in joint ones.

(b) _Partition driven by an increase in wives’ inheritance:_ Couples in reform states married after the reform should be less likely to be residing in joint families.

(c) _Partition driven by a decrease in husbands’ inheritance:_ Men in the reform states should switch to nuclear households if their income streams are potentially constrained by the reform.

In the next section, I empirically examine whether both these channels are in operation. I do not make any claims about the relative importance of the two. I first provide evidence for the partition channel and then explore whether the bargaining channel is in effect as well.

# VI.  EMPIRICAL EVIDENCE FOR CHANNELS

The NFHS data allows me to determine the family status of each married woman (joint or nuclear). I observe a list of all the members of each household in the Household Schedule of the data, and whether each of them is a usual resident in the household, which lets me identify which women live in the same establishment with family elders (i.e., a joint family for my purposes). A _joint_ family is defined as a family where a parent-in-law of a woman is present in the household;<sup>22</sup> the dummy

> 21. Botticinni and Siow (2003) shows that parents would want to leave bequests to those children who reside with them.

> 22. For instance, the woman identifies herself as the daughter-in-law of the household head or the woman reports she is the wife of the head and the parents of the household head are usual residents in the same establishment.

15
# DEBT MATURITY AND FIRM PERFORMANCE: **A PANEL STUDY** OF **INDIAN PUBLIC LIMITED** COMPANIES

by Fabio Schiantarelli (Boston College and The World Bank) and Vivek Srivastava (Govt. of Himachal Pradesh) Paper prepared for the project: "Term Finance: Theory and Evidence", Finance and Private Sector Development Division, Policy Research Department, The World Bank, Washington D.C., June 14, 1996. We would like to thank G. Caprio Jr, M. Dailami, A. Demirguc-Kunt, L. Sembet and other conference participants for useful comments.
21

|**_Tab_**<br>**_Estimated Long-Run Coeffi_**<br>**_1950_**|**_le 9_**<br>**_cients for th_**<br>**_– 2002_**<br>|**_e GDP per c_**<br>|**_apita_**<br>||
|---|---|---|---|---|
||AIC|RBSC|SBC|HQC|
|Private Capital Stock per capita (in logs)|0.30*<br>(0.10)|0.29*<br>(0.10 )|0.30*<br>(0.10)|0.30*<br>(0.10)|
|Public Capital Stock per capita (in logs)|0.71*<br>(0.11)|0.72*<br>(0.12)|0.71*<br>(0.11)|0.71*<br>(0.11)|
|Gov. Expenditures: subsidies per capita (in<br>logs)|-0.04**<br>(0.02)|-0.03***<br>(0.02)|<br>-0.04**<br>(0.02)|-0.04**<br>(0.02)|
|Gov. Expenditures: consumption per capita (in<br>logs)|0.11<br>(0.06)|0.10<br>(0.06)|<br>0.11<br>(0.06)|0.11<br>(0.06)|
|Gov. Expenditures: social security and<br>assistance transfers (in logs)|0.004<br>(0.061)|-0.04<br>(0.07)|0.004<br>(0.061)|0.004<br>(0.061)|
|Tax Revenue to GDP Ratio|-1.01**<br>(0.37)|-0.82**<br>(0.35)|-1.01**<br>(0.37)|-1.01**<br>(0.37)|
|Total Debt to GDP Ratio|0.30*<br>(0.09)|0.32*<br>(0.08)|0.30*<br>(0.09)|0.30*<br>(0.09)|
|Constant|-0.29<br>(1.00)|0.03<br>(1.12)|-0.29<br>(1.00)|-0.29<br>(1.00)|
|Trend|-0.002<br>(0.003)|<br>-0.001<br>(0.003)|-0.002<br>(0.003)|-0.002<br>(0.003)|
|Standard errors in (). *Significant at the .01 level.** S<br>level|ignificant at|the .05 level. *|** Significa|nt at the .10|

Government expenditure in consumption or social security has no statistically significant effect on per-capita GDP, while subsidies have a negative impact. The positive effect of public debt ratio is somewhat puzzling and could reflect an endogeneity problem i.e. that as GDP per capita increases there is a larger demand for financial assets and public bonds is one of those assets that domestic agents demand. To examine this hypothesis, we used Granger causality tests and the Wu-Hausman exogeneity test and both lead to the non-rejection of the exogenous public debt hypothesis.

In the short run (Table 10) private capital has a greater impact on GDP per capita than the public capital. Government expenditures have no effect on GDP, and tax rates have a negative impact on GDP. Public debt has also negative impact on GDP per capita in the short-run.

The long run results are puzzling for two reasons. First, because the high public capital elasticity and, second, because the fact that the public sector elasticity is higher than the private one. This fact is also present in several of the classic studies for the US and OECD economies, such as Aschauer(1989), Ram and Ramsey (1989), Eisner (1994), Sturn and de Haan (1995), Balmaseda (1997) and Viverberg (1997). Hurlin (2001a, 2001b) shows that, in general, papers based on time series analysis of variables in levels, like the present one, tend to find large output elasticities of public capital. Hurlin shows that there are two potential sources of bias for this finding: a) the endogeneity of the factors of production, i.e. the fact that the productivity of private capital may depend on the level of public capital; and b) the fact that in most of those studies the output and the inputs are not cointegrated and the variables are non-stationary leading to the spurious regression problem.
current employment status. The latter is captured by dummy variables for (i) working full-time, (ii) working part-time, (iii) unemployed and (iv) retired, with (v) others being the residual category. Employment status might influence the affordability of the tenure status beyond income. Finally, and most importantly, we include household income, expecting a higher likelihood of owning than renting as household income increases, as well as a higher likelihood of owning without a mortgage.<sup>15</sup> We also use the regression analysis to test several hypotheses on access to mortgage and its covariants. Traditional mortgage lending criteria base the mortgage approval and amount on current employment and income. From the consumer’s perspective, however, the mortgage should be based on life-time income and thus income expectations over the course of the mortgage. One way to test what determines mortgage finance access would be by examining the coefficients on income, age, and education and their interactions. If households are able to borrow only on their current income, then we should find a positive and significant coefficient on income, while insignificant coefficients on education and its interaction with age or income. If households are able to borrow based on their expected life-time income, we expect a smaller coefficient on current income and a negative coefficient on its interaction with education; while the coefficient on age might be positive, its interaction with education should be negative. This implies, obviously, that higher education is significantly correlated with higher income growth over the lifetime.

Table 2 provides descriptive statistics on the different household characteristics for 2007. Appendix Tables A1 and A2 report descriptive statistics for 2005 and 2006, respectively. We note that the average age is around 50 years. The share of female respondents is higher in the new EU countries, where the household size is also greater, but average income is – not surprisingly – lower. The share of respondents with secondary education is higher in the new EU countries, while the share

> 15 As for some surveys some of the categories are very small or some of the dummy variables are highly correlated with each other, in some cases we had to drop some variables from the specification in order for the model to converge.

16
Table 3. Croatia. Changes in Poverty on account of taxes and transfers

|Market income +<br>old-age pensions|Disposable<br>income|Consumable<br>income|
|---|---|---|
|(1)|(2) = (1) - direct<br>taxes - social<br>contributions +<br>direct transfers|(3) = (2) -<br>indirect taxes|
|**Poverty headcount**|||
|US $2.5 PPP-a-day per capita<br>5.2%|2.4%|5.9%|
|US $5 PPP-a-day per capita<br>10.3%|8.4%|17.5%|
|US $10 PPP-a-day per capita<br>28.1%|31.3%|50.0%|
|60% of median equivalized disposable income<br>20.4%|20.5%|28.8%|
|**Poverty gap**|||
|US $2.5 PPP-a-day<br>3.1%|1.2%|2.5%|
|US $5 PPP-a-day<br>5.3%|3.1%|6.8%|
|US $10 PPP-a-day<br>12.1%|11.0%|20.3%|
|60% of median equivalized disposable income<br>8.7%|7.0%|10.8%|
|**Poverty severity**|||
|US $2.5 PPP-a-day<br>2.3%|0.8%|1.6%|
|US $5 PPP-a-day<br>3.8%|1.8%|3.9%|
|US $10 PPP-a-day<br>7.5%|5.8%|11.4%|
|60% of median equivalized disposable income<br>5.7%<br>Source: Own estimates based on Croatian PIS and HBS 2014.|3.7%|5.9%|

To the extent that income is underreported in household surveys, one could imagine that this result is overestimated as households may actually have higher incomes than reported. In fact, there is evidence that this is the case in Croatia. However, the overall result that poverty increases with indirect taxes is robust to alternative measures of welfare, including those that use consumption as the basis for welfare measurement. Regardless of whether we measure welfare using disposable income in the PIS or HBS surveys, or consumption in the HBS survey, we find that the poverty rate increases once indirect taxes are taken into account such that consumable income has a higher level of poverty relative to disposable income (Figure 5A and Appendix 4). Finally, given that households do not observe the value of the benefits of in-kind education and health, we refrain from measuring poverty including those benefits, as standard in the literature. In terms of household type, Figure 5B shows that the impact of indirect taxes on poverty was particularly large among households with children and among retirees.

16
underestimate.<sup>17</sup> When presenting our estimates and comparing them to those from other countries, we will show that our conclusions are robust to adjusting for this underestimation by assuming a reasonable range of values for the missing services expenditures.

The last item, organizational structure is composed of a purchased and an own-account portion. The purchased part is estimated as the revenue of the management consulting services industry, multiplied by the sector coefficients from the input-output matrix. The own-account portion is estimated as one-fifth of wages of persons employed in executive occupations.

# _(2) Price deflators_

Unlike CHS, who use industry output deflators, we use the Consumer Price Index (calculated by IBGE) to deflate intangible expenditure. As discussed, the use of final output deflators is controversial because it is not the price index for the intangible, but that for the good that uses the intangible asset as an input. Since the major input into the production of intangibles is labor, and since the cost of labor (wages) should track the CPI, the CPI could in principle do a better job in capturing the changing price of intangibles. We stress that this issue is far from settled even in developed countries, and that there are ongoing initiatives to design better price deflators for intangibles (Aizcorbe et al. 2009).

# _(3) From expenditure to investment_

As discussed in CHS, not all expenditure on intangible items should be classified as investment in longlasting knowledge capital. For some intangibles, economic research suggests that only a portion of the spending on an intangible pays off in a future year (or years). For example, although the marketing literature finds that the effects of advertising are generally short-lived, some advertising has an effect well beyond its first year. In contrast, economic research suggests that all R&D spending has long-term benefits. We follow previous studies in assuming that only certain percentages of the expenditures estimated are to be treated as investment: 60% of expenditures on brand equity, 80% of own-account organizational structure expenditures and 100% of all other assets. These percentages from CHS are

> 17 Two alternate approximations based on the available spending on training in manufacturing that we explore in the results section are to assume that Brazilian spending on training in services is either in proportion to the ratio of value added in manufacturing and services, or that the ratio of labor training spending to that on brand equity and organizational improvement in services is the same as that in a comparator country.

15
_Rural-urban gradient in Latin America and the Caribbean_ _page_ 13

# **Table 4.  Proportion of total population, by land category and low population density**

||**Land ca**<br>**Suitab**<br>**rainfed**<br>**not for**|**tegory**<br>**le for**<br>**crops,**<br>**ested**|**Unsuita**<br>**rainfed**<br>**not for**|**ble for**<br>**crops,**<br>**ested**|**Good for**<br>**crops, f**|**rainfed**<br>**orested**|**Unsuita**<br>**rainfed**<br>**fore**|**ble for**<br>**crops,**<br>**sted**|
|---|---|---|---|---|---|---|---|---|
||**All**|<br>**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|**All**|**< 150 /**<br>**sq. km.**|
|Argentina|76%|<br>28%|21%|16%|2%|2%|1%|1%|
|Bolivia|28%|<br>10%|50%|31%|16%|11%|5%|3%|
|Brazil|66%|33%|19%|10%|10%|8%|5%|3%|
|Chile|21%|7%|60%|24%|7%|4%|12%|9%|
|Colombia|20%|10%|55%|18%|7%|6%|18%|9%|
|Costa Rica|5%|5%|12%|8%|7%|7%|76%|26%|
|Cuba|82%|41%|3%|2%|8%|7%|8%|5%|
|Ecuador|60%|22%|35%|18%|2%|2%|3%|2%|
|Guatemala|7%|<br>5%|50%|10%|7%|5%|36%|15%|
|Guyana|25%|<br>12%|9%|3%|52%|41%|14%|8%|
|Honduras|19%|8%|28%|16%|11%|6%|42%|23%|
|Mexico|42%|<br>13%|35%|19%|10%|4%|12%|7%|
|Nicaragua|42%|19%|29%|19%|9%|6%|20%|12%|
|Paraguay|68%|<br>22%|3%|2%|25%|19%|4%|3%|
|Peru|8%|<br>6%|80%|33%|7%|5%|5%|4%|
|Panama|31%|<br>16%|19%|10%|13%|6%|38%|23%|
|Uruguay|98%|58%|2%|2%|0%|0%|0%|0%|
|Venezuela|48%|<br>19%|17%|6%|22%|10%|13%|4%|
|_LCR Totals_|_48%_|<br>_20%_|_32%_|_14%_|_10%_|_6%_|_11%_|_5%_|

Sources: 1) Population and density are from CIESIN Gridded Population of the World 3.

2) Rainfed crop suitability data are from FAO/IIASA Global Agro-Ecological Zones. Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total population as given by the tabulated cells, and excluding those few cells with omitted country codes.

4) “Unsuitable” include GAEZ classifications marginal, very marginal and not suitable.
**Figure 5 OECD index of regulatory restrictions in legal services**

<!-- Start of picture text --> Greece<br>Turkey<br>Tunisia<br>Czech republic<br>Slovak Republic<br>Spain<br>Germany<br>Italy<br>Portugal<br>Japan<br>New Zealand<br>Canada<br>Luxembourg<br>Switzerland<br>Hungary<br>Ireland<br>France<br>Norway<br>Korea<br>0 1 2 3 4 5<br>Restrictiveness index<br><!-- End of picture text --> Data source: Based on OECD (2005a).

27
Policy Research Working Paper 9146

# **Abstract**

This paper presents a review of studies that estimate the cost of achieving the Sustainable Development Goals. Although the Sustainable Development Goals provide useful benchmarks for fiscal authorities and donors, typical cross-country costing exercises can be misleading, for a variety of reasons: double counting, sensitivity to underlying assumptions, downplaying the critical role of policy and institutions in advancing toward the goals, failure to discount costs or consider operation and maintenance costs in a consistent manner, and overlooking the tendency for different types of Sustainable Development Goal–related spending to have distinct effects. Recent costing studies by the World Bank Group have been developed to minimize the drawbacks of earlier studies. The paper also briefly reviews how the World Bank Group engages with stakeholders on the Sustainable Development Goals agenda.

This paper is a product of the Equitable Growth, Finance and Institutions Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dvorisek@worlbank.org and syu2@worldbank.org.

_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 conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Similarly, carbon assets from land use (LULCF) projects are rare in the CDM market; their cumulative market share, in terms of volumes transacted, hardly reaches 0.2%.<sup>72</sup> This is largely due to their exclusion from Europe’s ETS.<sup>73</sup> Even so, this is striking and viewed by many as a failure of current policies since emissions from deforestation and land degradation account for an estimated 18 to 25 percent of all global greenhouse gas emissions.<sup>74</sup> To a degree, obstacles for including forestry projects under the Kyoto mechanisms have given rise to projects in the voluntary markets that emphasize additional biodiversity benefits and other positive spillovers. Examples include the World Bank’s Forest Carbon Partnership Facility, the Australia Global Forest Fund and Carbon Neutral Norway among others.

Carbon credits from clean energy projects comprise the greatest share of the JI market, with slightly less than two thirds of volumes transacted over 2003-2006. ERUs from energy efficiency improvement and fuel switching projects came first at 28%, followed by biomass, wind and hydro with respectively 13%, 12% and 10% of the market. N2O projects from industrial installations account for 8%. This picture could change notably in the coming years as Russia and Ukraine bring opportunities from the oil, gas and power sectors. The pipeline for JI indicates expected credits by 2012 from reducing fugitive emissions will come from pipelines (44%), emission reductions from energy efficiency improvement and fuel switching (32%) and coal mine methane (12%).<sup>75</sup> Unlike in developing countries, where green-field projects have long lead times, many such opportunities in JI countries are associated with existing facilities and sites and have relatively shorter lead times. Many such projects are likely to be implemented within the 2012 time-frame provided financing is available before the window of opportunity starts to close.

# **Who is buying project credits?**

European buyers dominated the primary CDM and JI market with 86% market share (versus 50% in 2005) with Japanese purchases sharply down at only 7% of the primary market in 2006 (versus 46% in 2005). Within Europe, the United Kingdom had a 50% market share of volumes transacted (up from 15% in 2005) consolidating its leadership position as the carbon finance hub for the world. Many companies, including project developers and players with an eye on the secondary market, have opened accounts on the U.K. national registry. Private sector players were the main buyers of CDM assets in 2006, with about 90% of purchases coming from the European private sector in 2006. In

> 72 See Neeff et al. (2007) for a recent and exhaustive review of the market for forestry offsets.

> 73 See a related discussion by Schlamadinger et al. (2005).

> 74 As of end of January 2008, the UN reported fifteen land-use projects in the CDM pipeline.

> 75The term “fugitive emissions” refers to pollutants released to the air other than those from stacks or vents. They can be occur due to equipment leaks, evaporative processes, and windblown disturbances.

47
37 The general capital rules (i.e., rules that have to be met by all insurance companies including those offering services in the second pillar) are as follows:

- The minimum initial capital is specified at a range from 5 to 10 million CHF, depending on the anticipated business volume.

- The s _olvency margin_ defines the own free capital that a life insurance company must hold, in case the amount of the necessary solvency margin exceeds the minimum initial capital. Its specific level depends on the business volume and the risk exposure of the insurer. The first component of the solvency margin amounts to 4 percent of the technical reserves times the fraction of the business volume not covered by a reinsurance company. The second component is a function of the different risk-capital positions that is not reinsured.

- To finance an extraordinary expansion of the business, the insurance company is obliged to invest up to 50 percent of the minimum initial capital in an _Organization Fund_ .

- Insurance companies have to legally bind certain assets to ensure the security of policyholders’ claims. The corresponding amount, which has to be invested in a so– called _Security Fund_<sup>35</sup> , is mainly determined by the legally required capital (i.e., the actuarial reserves) and is verified by the supervisory authority on a regular basis. In case of an under-funding of the security fund, the insurance company is obliged to cover the shortfall within one month. If the insurance company goes bankrupt, however, the insured workers and retirees have a privileged claim on the value of the security fund.

Table 11 provides an overview on the total provisions for the insurer’s own account<sup>36</sup> , and the percentage that is attributed to old–age provisions. In 2003, the total provisions for own account amounted to 229 billion CHF, of which 54 percent were related to old–age provisions. Table 11 also presents the total amount invested in the insurers’ security funds. The actual amount invested in the security funds always exceeds the required sum. Assets invested in the security funds are subject to special investment regulations and stricter valuation rules.

|**Table 11 Provisions for Own Account and the Security Fund**|
|---|

|in bn CHF|Provision<br>Total|s for Own Acc<br>Old-Age<br>Provision|ount<br>%|Sec<br>Required|urity Fund<br>Actual|%|
|---|---|---|---|---|---|---|
|1997|167,5|87,8|52.45%|161,5|181,7|112.53%|
|1998|188,6|96,6|51.20%|182,8|200,3|109.58%|
|1999|204,6|104,8|51.23%|195,4|211,5|108.25%|
|2000|217,4|114,1|52.47%|209,0|218,5|104.54%|
|2001|224,8|119,3|53.09%|215,4|221,9|103.03%|
|2002|228,2|122,3|53.61%|218,4|225,1|103.12%|
|2003|229,1|123,7|54.00%|221,0|228,8|103.54%|

Source: Bundesamt für Privatversicherungen (2003), p.64 sqq.

> 35in German: Sicherungsfonds

> 36 In German: Versicherungstechnische Rückstellungen für eigene Rechnung