rafmacalaba/datause-encoder-data huggingface dataset:
India has had a long history of legal activity to overcome a historical legacy of discrimination and high inequality, with varying levels of success.<sup>5</sup> Following a vigorous campaign, the prevailing inheritance legislation was amended nationally in 2005 to eliminate gender discrimination that had thus far prevailed in all but a few southern states that had amended the Act earlier. Specifically, the states of Maharashtra and Karnataka amended the Act in 1994 granting daughters equal shares in inheritance relative to sons that was denied to daughters in the past. The passage of sufficient time since the enactment of the amendment, and the availability of data over three generations of individuals provides an opportunity to assess the impact of the legal change on women’s asset endowment and their socio-economic outcomes. Specifically, we use data from the 2006 nationally representative Rural Economic and Demographic Survey (REDS) conducted by the Indian National Council for Applied Economic Research on 1371 rural Hindu households in the states of Karnataka and Maharashtra. The survey contains detailed information on parents, siblings and children of the household head, providing us with a quantitative measure for intergenerational transfers of both physical and human capital investments.

Our identification relies on a difference-in-differences strategy to estimate the impact of the Hindu Succession Act amendment, comparing the inheritance of land to males and females by fathers who died before and after the amendment of the Act in the states of Maharashtra and Karnataka. Our baseline results suggest that the amendment significantly increased women’s likelihood to inherit land although it was not effective in fully eliminating the underlying inequality. Furthermore, we find a robust increase in women’s age at marriage relative to men, suggesting an effect of stronger inheritance rights on women’s marriage market outcomes. Our results also point towards a positive and significant impact on women’s educational attainment. Girls who started their education after the amendment came into force had 0.3 years more of elementary education in 2006. Moreover, to the extent that estimated effects on women’s inheritance appear to increase over time, it can be attributed to learning about the content of the amendment, and would

> 5A range of progressive legislation in India that aimed to modify undesirable social practices such as dowry or caste discrimination that has been ineffective in practice (Anderson 2003) provides reason for caution.

4
Harris (1954), Hanson (2005), Emran and Shilpi (2012), Dorosh, Wang, You, and Schmidt (2012), Jedwab and Storeygard (2016), and Blankespoor et al. (2016).

> 3 We use 1 �∑��� ��,����,��� instead of ∑��� ��,����,���  so as to be able to calculate the natural logarithm of the market access index even when the weighted sum of the populations is equal to zero, which can occur as we restrict the calculation of the market access index to travel times of six hours or less.

> 4 We use time indexes _t_ and _t-_ 1 to refer to the years in our data (1970, 1980, 1990, 2000 or 2005; see Section 3). Note that in Formula (1), we exclude the population of the locality and use travel times based on roads prior to _t-_ 1 (see Appendix), which addresses endogeneity concerns in the regressions.

5 Major ports are defined as ports that include direct or trans-shipment capacity as measured in the AICD report (Foster and Briceño-Garmendia 2010).

> 6 The problem is attenuated by the fact that we use the _lagged_ value of the market access variable.

7 The number of people residing in each location is determined from LandScan and UNEP/GRID-Geneva. The urban/rural dichotomy is based on a density threshold. These information are given at the 30 arc second (approximately 1 by 1 km). The spatial model to allocate national or subnational GDP across space uses subnational population data from LandScan within urban and rural strata and does not make any direct use of roads or cropland. 8 The average market access index grows at 3.95 per cent annually.

9 We have 20.088-1�0.063.

> 10 The estimates show that a marginal increase in Δ���� ln ���,� has a smaller impact than a comparable increase in Δ���. ln ���,�. In our sample, the standard deviation in Δ���� ln ���,� is also small than the standard deviation in Δ���. ln ���,�, from which we conclude that changes in population had more impact on cropland expansion than road improvements.

11 Any measure of local GDP based on remote sensing, such as ours, is subject to measurement error (Chuhan-Pole, Dabalen, and Land, 2015). Even so, it is the best measure of local economic activity currently available and so we present it as suggestive evidence.

12 Because of possible endogeneity concerns due to the construction of the local GPD variable as a function of the surrounding population (see endnote 7 in Section 3), the market access variable may itself be endogenous. We therefore interpret it as a control included to attenuate omitted variable bias.

19
15

# Table 1: Number of Students and Schools in the Analytic Sample

||Number of|Number of|
|---|---|---|
|Type of School|Schools|Students|
|Coeducational Schools|20|492|
|Girls|-|149|
|Boys||343|
|Single-sex Schools|20|520|
|Girls|4|78|
|Boys|16|442|
|Total|40|1,012|

# Measures

Mathematics achievement. The mathematics test used as the major dependent variable was the forty-item SIMS "core" test. The curricular content of the SIMS test was decided upon by all country participants in the SIMS study, and items testing this content are constant across countries. The core test contained items covering five curriculum content areas (arithmetic, algebra, geometry, statistics and measurement). The score is total number of correct answers, with no adjustment for guessing.

Student background and attitudes. Student background variables include both conventional demographic measures -- sex, age, rural residence, and paternal occupation (professional or non-professional) -- as well as
# **<u>Appendix table 3: Smallholders’ socio-economic characteristics</u>**

||**Total**|**North **|**By region**<br>**Center **|**South **|
|---|---|---|---|---|
|**Household composition and head’s characteristics**|||||
|Number of children|2.23|2.48|2.26|2.12|
|Number of adults|2.36|2.72|2.38|2.23|
|Number of old people|0.18|0.19|0.18|0.19|
|% of female head|28.07|23.73|25.63|31.58|
|Head’s age|43.09|43.84|42.42|43.36|
|% of heads no schooling at all|26.12|11.33|28.38|29.52|
|% of heads with primary 1-5|25.99|20.04|27.37|26.98|
|% of heads with primary 6-8|28.56|37.41|27.83|26.02|
|% of heads with sec.& above|16.79|28.53|14.08|14.82|
|% of head born in own village|52.73|55.28|55.56|49.53|
|% with hh members did wage job|15.48|16.63|15.40|15.14|
|**Household assets**|||||
|Value of livestock (2006 US$)|99.00|241.30|76.17|67.32|
|Value of agric. assets (2006 US$)|84.14|333.72|44.78|27.79|
|% owned radio|64.11|70.32|61.45|64.06|
|% owned cell phone|12.87|19.53|11.19|11.89|
|% of grass roof|74.11|67.41|80.95|70.93|
|% of iron sheets roof|25.05|31.70|18.21|28.24|
|% of sand floor|4.26|0.89|3.46|6.10|
|% of smoothed mud floor|78.07|74.06|82.67|75.76|
|% of smoothed cement floor|17.43|24.67|13.71|17.88|
|% of mud walls|8.95|10.58|9.97|7.55|
|% of compacted earth walls|17.47|23.95|33.86|1.91|
|% of mud brick walls|33.48|10.46|26.66|47.16|
|% of burnt brick walls|37.50|51.10|27.42|40.86|
|**Distance to estates and community characteristics**|||||
|Km to the nearest ag. estate|2.10|2.16|1.44|2.61|
|% within ag. estate|8.72|7.61|16.26|3.03|
|No. of obs.|20,677|3,379|7,732|9,566|

_Source:_ Own computation from 2006/07 NACAL.

29
26 Morck, R. and B. Yeung (2002) “The puzzle of the harmonious stock prices,” _World Economics_ , Vol. 3 No. 3.

Ngugi, R.W, V. Murinde, and C. Green (2003) “How have the emerging stock exchanges in Africa responded to market reform?”, _Journal of African Business_ 4:2:89-127. Purfield, C., H. Oura, C. Kramer, and A. Jobst (2006) “Asian equity markets: growth, opportunities, and challenges,” IMF Working Paper WP/06/266.

Schwert, G.W. (2003) “Anomalies and market efficiency”, in _Handbook of the_ _Economics of Finance_ , Vol. 1B Edited by G.M. Constantinides, M. Harris, and R. Stulz, Elsevier North Holland.

Standard and Poor’s. 2005. “Corporate Governance Disclosures.” Studies of Hong Kong, Indonesia, Malaysia, Singapore, and Thailand, produced jointly with the Corporate Governance and Financial Reporting Center.

Tobin, J. (1982) “On the efficiency of the financial system,” _Lloyd’s Banking Review_ (July).
2

# **Table of Contents**

|Intro|duction|p. 3|
|---|---|---|
|I.|Property Rights and Incentives|p. 5|
||A. General Framework|p. 7|
||B. Taxonomy of Property Rights|p. 9|
||i. Open Access Common Resources|p. 9|
||ii. Restricted Access Common Resources|p. 10|
||iii. Usufructs|p. 11|
||iv. Private Property Rights|p. 12|
||v. Restrictions on Private Property Rights|p. 13|
||C. Protecting Entitlements with Liability Rules|p. 15|
|II.|Transaction Costs, Contracts and Organizations|p. 17|
||A. Sources of Transaction Costs|p. 18|
||B. Enforcement Costs|p. 21|
||C. Firms and Markets|p. 22|
||D. Influence Costs|p. 23|
|III.|Government Regulation and Government Provision|p. 25|
|IV.|Culture, Religion and Property Rights|p. 30|
|V.|Empirical Approaches and Conclusion|p. 36|
||A. Cross Country Studies|p. 36|
||B. The Comparative Institutionalist Approach|p. 38|
||C. The Micro-Econometric Approach|p. 39|
|Bibliogra|phy|p. 43|
# **Growth of Global Corporate Debt: Main Facts and Policy Challenges**

Facundo Abraham Juan J. Cortina Sergio L. Schmukler<sup>*</sup>

**JEL Classification Codes:** F32, F34, F65, G0, G10, G15, G30

**Keywords:** capital raising, corporate bonds, corporate financing, currency risk, debt, emerging economies, foreign currency debt, global financial crisis

> * This paper was written for the Oxford Research Encyclopedia of Economics and Finance, edited by A. Dixit, S. Edwards, J. Hamilton, K. Judd, and K. Kletzer. We are grateful to Marta Guasch for excellent research assistance and to Ken Kletzer for helpful comments and for encouraging us to write the paper. Research support came from the World Bank Chile Research and Development Center, Knowledge for Change Program (KCP), and Strategic Research Program (SRP). The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Email addresses: fabraham@worldbank.org, <u>jcortinalorente@worldbank.org, sschmukler@worldbank.org.</u>
of non-preferential exports face MFN tariffs greater than 5 percent but for China (along with Japan and the EU‘s external trade) the proportion is twice as high (WTO, 2011).

**Table 4:  Preferential trade by exporter, 21 large exporters**

||Share of<br>|trade by pre<br>|ferential ma<br>|rgin(PM)|<br>and MFN rate|<br>(inper cent of t|<br>otal trade)|
|---|---|---|---|---|---|---|---|
|Et|Pre|ferential exp|orts|Non|-preferential|exports|MFN Zero|
|xporer||||||||
||Total|PM above<br>5%|PM below<br>5%|Total|MFN above<br>5%|MFN below<br>5%|Total|
|TOTAL|16.3|5.7|10.5|30.2|11.3|18.9|52.3|
|China|5.5|0.6|4.8|38.4|19|19.3|55.4|
|EU-extra|13.1|5.2|8|42.1|18.4|23.7|43.1|
|United States|21.7|19.5|2.2|30.2|10.4|19.8|46.5|
|Japan|0.5|0.3|0.2|57.7|24.7|32.9|39.6|
|Canada|45.7|7|38.7|7.5|2.8|4.7|46.4|
|Korea,Rep. of|7.7|0.1|7.7|39.5|12.9|26.5|51|
|Russian Federation|5.7|0.1|5.6|16.5|4.1|12.4|77.6|
|Mexico|63.9|13.2|50.7|1.8|0.8|1|34|
|Malaysia|14|2.7|11.3|14.1|5.3|8.9|70.2|
|Switzerland|34.1|6.6|27.6|14.9|9.7|5.3|48.9|
|Australia|2.9|0.1|2.7|21.1|8|13.1|75.4|
|Singapore|18.9|6.2|12.6|13.3|2.8|10.5|66.4|
|Thailand|26.8|6.9|20|15.1|8.4|6.7|54.1|
|India|25.9|2.6|23.3|24.7|9.9|14.8|48.3|
|Brazil|15.3|4.3|11|26.4|8.5|17.9|56.9|
|Norway|18.1|8.2|9.9|8.7|3|5.8|72.9|
|Indonesia|20.6|4.3|16.2|15.6|8|7.7|61.3|
|Turkey|64.8|38.6|26.2|16.9|5|11.8|17.6|
|South Africa|21.5|3.4|18|15.1|5.8|9.3|62.4|
|Philippines|11.6|2.9|8.8|7.6|4.2|3.4|78.9|
|Chile|27.3|11.5|15.9|7|2|5.1|63.5|

Source: Based on WTO (2011).

Many preferential agreements, including some of China‘s agreements, now also have a services dimension. Fink and Molinuevo (2008) and Marchetti and Roy (2008) have shown that these agreements typically involve wider and deeper commitments than those which countries have made under the General Agreement on Trade in Services (GATS) at the WTO. In other words, countries include more sectors and legally commit to greater openness in preferential agreements than they do multilaterally. But most of these commitments hover above applied policy and very few have had a liberalizing impact. For example, India has committed to allowing maximum foreign ownership of 25 percent in basic telecommunications under the GATS, to 49 percent in some of its preferential agreements, but in practice it already allows 74 percent. Even in the rare cases, where preferential agreements have induced liberalization, say in Costa Rica‘s elimination of its telecommunications monopoly, the new policies are at least in principle applied on a nondiscriminatory basis. Thus the cost of exclusion today from a preferential agreement in services 19
**Figure 3: Change in Mineral Production Values between 2002 and 2006 calculated using 2001 production quantities and changes in prices**

<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25<br>Regions<br>Change in prices x mineral production in 2001<br>50<br>40<br>30<br>20<br>10<br>Change in unanticipated mining revenues<br>0<br><!-- End of picture text --> <!-- Start of picture text --> Change in unanticipated mining revenues<br><!-- End of picture text --> <!-- Start of picture text --> Change in prices x mineral production in 2001<br><!-- End of picture text --> Source: London Metal Exchange and London Bullion Market Association and NGA, annual reports, Oil Patch Research Group, U.S. Geological Survey, US Energy Information Administration and Ministry of Energy and Mines (2002–2006) 6
Another important issue emerging from the conceptual framework of Figure 1 revolves around the exploration of the dynamic relationship between (im)mobility and adaptation outcomes, which has important implications for rural development and local adaptation policies. Exploring this nexus presupposes the availability of both mobility and adaptation outcomes. While we have just illustrated that household surveys are generally well-equipped to look at household-level migration, a key question is whether it is also possible to build a reliable adaptation indicator using their data. The ideal adaptation indicator we have in mind is a household-level indicator of _in situ_ adaptation – which excludes _ex situ_ adaptation via migration – able to capture both on-farm adaptation through the use of modern agricultural practices and investment in inputs and the ability of the household to diversify income away from agriculture. For this purpose, having access to data from an agricultural questionnaire is essential, meaning that building such an index is currently feasible only for household survey collections – such as the LSMSISA – with a specific focus on agriculture. In practice, this index can be built using standard econometric tools such as factor or principal component analysis starting from raw data variables. To illustrate this point, we stick with the Nigeria dataset employed above and show, in Table 2, descriptive statistics for a type of _in situ_ adaptation score built out of the Nigeria Universal Panel Data. We also report descriptive statistics for the raw data variables that enter the estimation model.

**Table 2: Adaptation statistics using the Nigeria Universal Panel Data (2010-2019)**

|**Variable name**|**Mean**|**SD**|**Min**|**Max**|**Obs**|
|---|---|---|---|---|---|
|_In situ_adaptation score|31.371|17.264|0|100|11,262|
|Share of irrigated plots|0.020|0.127|0|1|11,389|
|Share of plots on which inorganic fertilizers were used|0.380|0.461|0|1|11,503|
|Share of plots on which organic fertilizers were used|0.133|0.326|0|1|11,503|
|Share of plots on which herbicides fertilizers were used|0.292|0.436|0|1|11,474|
|Share of plots on which pesticides were used|0.157|0.347|0|1|11,474|
|Number of plots owned by the household|2.311|1.403|1|13|11,536|
|Non-farm enterprise (yes=1)|0.568|0.495|0|1|11,564|

26
# 6 SUMMARY AND CONCLUDING REMARKS

Flows gf funds between sectors, The savings which each economic sector puts aside out of its income each year may be applied to the acquisition of real or financial assets. A sector which acquires real assets in excess of its savings has to sell or issue financial assets. The difference between a sector's net saving and its net acquisition of real assets is referred to as its financial surplus or deficit, or more simply as its borrowing or lending. Information on the borrowing and lending of the various sectors are contained in the flow of funds, or financial accounts, statistics which are available for most of the industrial cour.tries, and for a small number of developing countries.

A review of these statistics for seventeen developing countries reveals that the household sector is a net lender in almost all cases. The business sector is almost always a net borrower sector. The government sector is sometimes a net lender, but more often a net borrower, while the opposite is true for the foreign sector.

Self financing ratios, Besides comparing the sectoral financial surpluses to GNP, it is of interest to know what proportion of each sector's acquisition of real assets is financed out of its own saving. On average, the household sector saves more than twice the amount needed to finance its accumulation of real assets; the remainder being lent to other sectors.

The ratio for the business sector is the most interesting. Overall about one-half of real capital formation by business is externally financed. This would be consistent with a picture in which the business sector continually generates worthwhile investment opportunities in excess of its ability to 56
- 21 -

# **Table 1.** **<u>Summary statistics</u>**

|Variable|Description|Obs|Mean S|td. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|**Choice-specific vari**|**ables:**||||||
|Crop price|Average crop sale price in a 50km radius; if<br>not available, the price at the closest locality is<br>used (TSh/kg)|2475|<br>0.16|0.35|0.0004|<br>2.18|
|Crop suitability|Potential crop yield estimated by SPAM<br>Update 2010 (ton/ha)|2475|<br>1.38|0.90|0.00|<br>8.42|
|Transport cost|For export crops, transport costs to convey<br>goods to the port; for domestic food crops,<br>transport costs to the nearest city with more<br>than 100,000 population (US$/ton)|2475|15.28|33.25|0.01|182.86|
|Access to fertilizer|Fertilizer used for each crop by neighboring<br>farmers within a 50km radius (kg)|2475|<br>0.40|2.25|0.00|50.00|
|Access to irrigation|Number of neighboring farmers using<br>irrigation within a 50km radius|2475|<br>1.71|7.62|0|<br>111|
|Access to improved|Dummy variable for the existence of any||||||
|<br>seed|<br>neighboring farmers using improved seed<br>within a 50km radius|2475|<br>0.38|0.49|0|<br>1|
|**Individual-specific v**|**ariables:**||||||
|Area of plot|Area of land planted (ha)|2475|<br>1.44|1.93|0.00|42.00|
|Steep slope|Dummy variable for the land gradient referred<br>to as slightly sloped or very steep|2475|<br>0.33|0.47|0|<br>1|
|Land title|Dummy variable for land title held for each<br>plot|2475|<br>0.10|0.30|0|<br>1|
|Electricity access|<br>Dummy variable for households with access to<br>electricity (including solar and self-generation)<br>for lighting|2475|<br>0.06|0.23|0|<br>1|
|Mobile access|Dummy variable for households that spent<br>some money on mobile phones in the last one<br>month|2475|<br>0.54|0.50|0|<br>1|
|Distance from road|Distance from a road (km)|2475|<br>1.44|2.55|0.00|28.00|
|Male household head|Dummy variable for male-head household|2475|<br>0.85|0.35|0|<br>1|
|HH head age|Age of household head|2475|45.90|13.53|18|<br>92|
|HH head's year of|Number of years of education that household||||||
|schooling|head attained|2475|<br>6.27|2.11|1|<br>17|
|Household size|Number of household members|2475|<br>6.23|3.42|1|<br>35|
|HH wage income|Total amount of household wage income (TSh<br>million)|2475|<br>0.15|1.39|0.00|51.15|
|**Instrumental variab**|**le:**||||||
|Crop price 2008|Average crop sale price in a 50km radius in<br>2008; if not available, the price at the closest||||||
||localityis used(TSh/kg)|2475|<br>0.18|0.35|0.005|<br>1.96|
Land titling, in the form of land-use certificates (LUCs), was an important element of the 1993 land law. Figure 11 gives the proportion of land with a LUC.<sup>40</sup> We see that LUCs are less common among the poor. Again we see that it is the relatively well-off within rural society who appear to be benefiting from the new land market.

# _5.2 Decomposition of the change in landlessness_

The aggregate landlessness rate is obtained by integrating the conditional landlessness rate across the distribution of consumption. The overall rise in landlessness in rural areas thus reflects both a shift in the relationship between land holding and consumption — the economic gradient in landlessness studied in section 3 — as well as a shift in the distribution of consumption. This can be seen if we divide the population into _m_ consumption groups and decompose the change in the proportion of landless between 1993 and 2004 as follows: Here _LLti_ is the landlessness rate for consumption group _i_ =1,.., _m_ at date _t_ while _nti_ is the proportion of households in group _i_ at date _t_ . The first term on the RHS gives the contribution of the change in the relationship between landlessness and consumption — the “land reallocation” component — while the second term gives the contribution of changes in the distribution of consumption — the “consumption redistribution” component.

Table 3 gives the decompositions, based on fractiles of 1993 consumption per person.<sup>41</sup> We find that the change in the relationship between landlessness and consumption increased the overall landlessness rate by 2.3% points, while the change in the distribution of consumption

> 40 To derive the share of land with a LUC we aggregated plot-specific responses, weighted by plot sizes.

> 41 Given the large shift to the right in the distribution of consumption we had to choose fractiles carefully, to avoid small sample size.  The precise fractiles by region are given in the Appendix.

26
inheritance rights for daughters and sons has increased female foeticide, excess female infant mortality, and son-biased fertility stopping (Bhalotra, Brule, and Roy 2017; see also Rosenblum 2015). Kulkarni concluded from his work on colonial India that “egalitarianism requires egalitarians.”

# **_Policy argument_**

The policy argument of this essay is that law can indirectly bring about a change in cultural categories and other schemas; in turn, this can change societies. A category is largely defined by prototypes (Rey 1999, Smith and Medlin 1999). We will show that widespread exposure to new exemplars and social patterns changes the meaning of a category. It can also change the contexts that activate particular cultural categories.

Starting from a stable system with “matched” institutions and cultural schemas (Lieberman 2002), to use law to change society requires a one-two punch, defined as “two forces combining to produce a marked effect.”<sup>6</sup> The two forces are a change in rights and prohibitions (the _command and coordination function_ of law) and a change in experience or exposure that creates cognitive foundations for the new rights (the _schematizing function_ of law). Like the one-two punch in a boxing match, the two forces independently might have little impact but together could have a large impact.

> 6 Online _Merriam-Webster Unabridged._

8