rafmacalaba/datause-encoder-data huggingface dataset:
Inventory of Public Land in Ahmedabad, Gujarat, India

# **1 Introduction**

The urgent need for substantially augmenting financial resources required for investing in urban infrastructure is well recognized. 1 Monetizing public land assets could contribute a substantial share of funds needed, as international experience has shown in other countries.<sup>2</sup> The 13<sup>th</sup> Finance Commission of India has underlined the importance of ensuring proper use of land held by governments, central, state and local as well as government-owned Public Sector Undertakings.<sup>3</sup> The Committee on Fiscal Consolidation has noted that there is considerable potential given the underutilized prime land of Public Sector Undertakings, Port Trusts, and Railways, etc. For realizing such potential, the Committee has considered it as a part of disinvestment and has recommended setting up of a group to work out the policy framework and institutional modalities.<sup>4</sup> The High Power Expert Committee (HPEC) has also recognized that monetizing underutilized public land can be a source of generating finances. The Committee has called for judicious and transparent use of the instrument of unlocking land value and has recommended that the following steps be taken before the ‘sale of land’ is used as an instrument for financing urban infrastructure:

- “A systematic city-wide inventory of land assets must be made to be able to identify core and non-core land assets, and proposing the best use of public land assets must be part of comprehensive planning for the city.

- A transparent and accountable mechanism for sale of public land must be put in place.

- Proceeds from land sales must be used only for capital investment projects/housing for the poor via creation of a ‘Land Capital Fund’ whose governance and operational mechanisms are designed in such a manner as to ensure total transparency.

- A mechanism for sharing revenues between the public agency owning the land and the infrastructure development agency must be established.”<sup>5</sup>

The World Bank and PPIAF, in collaboration with the India Development

> 1 High Power Expert Committee (HPEC): _Report on Indian Urban Infrastructure and Services_ March 2011

> 2 George Peterson: _Unlocking Land Values to Finance Urban Infrastructure_ . The World Bank Public Private Infrastructure Advisory Facility, Washington D.C. 2009

> 3 _Report of the 13th Finance Commission 2010-2015, Volume I_ December 2009

> 4 _Report of the Committee on Fiscal Consolidation, Ministry of Finance, Government of India_ www.finmin.nic.in

> 5 HPEC _op.cit._

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the initial drop in the level of output below trend in a downturn ("normal" recessions associated with the business cycle, as opposed to a "severe" contraction induced by an economic crisis) may generate asymmetries in the response of poverty to output shocks. The first part of the paper provided a review of several alternative channels through which output shocks can have nonlinear effects on poverty. They include expectations and confidence factors; credit rationing at the level of firms; borrowing constraints at the household level; and the "labor hoarding" hypothesis. The "credit crunch" argument is based on the view that higher interest rates (which may be implemented in the context of an economic crisis to defend the exchange rate) do not only have a negative demand and supply-side effects; they also tend to discourage less risky potential borrowers. If lenders have no adequate way of discriminating between high and low-risk borrowers and internalize this adverse selection problem, a marketclearing equilibrium may not exist, and instead there may be persistent credit rationing-particularly of small and medium-size enterprises, prepared to pay higher interest rates, but unable to obtain any funds. The second part proposed a simple vector autoregression technique (involving the detrended components of output, real wages, the unemployment rate, and the poverty rate) to test for the existence of the two types of asymmetric effects described earlier. The third part applied this technique to Brazil, using annual data for the period 1981-99. The results of impulse response analysis indicated that the response of the poverty rate to output shocks is state dependent; when output is initially above trend, any positive shock tends to lower poverty. By contrast, in a recession or in a period of severe contraction, output shocks have no discernible effect on poverty-and neither do they affect unemployment.

There are several directions in which the present study can be extended. The first is to broaden the sample to consider a time-series, cross-section dataset of "economic crises" and a comparative sample of "tranquil times," in order to assess (using possibly panel VARs) the extent to which the results reported here for Brazil hold more generally. Another direction is to abandon the "standard" VAR approach and use a vector error correction model that integrates 20
available in the region where they were born. By the same token, Dahl and Sorenson (2011) found that companies perform better, survive longer and generate higher profits when located in regions in which their founders have lived longer, this effect being similar in size to that associated to previous experience in the same sector.

Following this line of reasoning, both spinoffs (entrepreneurs leaving a mother firm to found a new business<sup>27</sup> ) and ‘serial entrepreneurs’ (founders who have previously run other businesses) may have an advantage compared with “ _de novo_ ” entrepreneurs. For example, Hirakawa, Muendler and Rauch (2010), using microdata from Brazil over the 1995-2001 period, found that spinoffs are characterized by larger entry sizes (see Section 4.2.1) and lower exit rates than new firms not generated by a parent company. Similarly, the role of past experience and path-dependence is confirmed by the fact that serial entrepreneurs are more likely to replicate the success of their past companies than single venture entrepreneurs or serial entrepreneurs who failed in their prior business (see Gompers _et al_ ., 2006).

Turning our attention to a managerial perspective, new founders who had previously been employed as top managers in the same sector and who had better access to relevant information are expected to exhibit better post-entry business performance (for an empirical validation of these relationships, see Cooper, Gimeno-Gascon and Woo, 1994; Cressy, 1996; Arrighetti and Vivarelli, 1999; Lee and Tsang, 2001; Shane, 2001; Vivarelli, 2004).

Some studies confirm the positive link between previous job experience and a firm’s postentry performance in the case of DCs too. For example, McPherson (1996) found a positive relationship between annual employment growth and previous experience of the founder in similar economic activities for entrepreneurial firms in Swaziland and Botswana, while Vijverberg (1991) and Goedhuys and Sleuwaegen (2000), both studying Côte d’Ivoire, found that job experience previously acquired in the same industry both increases the likelihood of founding a new business and contributes to a firm’s better performance.

> 27 For instance, Sørensen and Phillips (2011) argue that work experience in the prior firm shapes both the entrepreneur's competence and his/her commitment to the entrepreneurial role. However, while competence and information inherited from the mother firm provide an initial advantage, parental influence may generate inertia and resistance to change, unless the new company is able to create its unique competitive identity (see Ferriani, Garnsey and Lorenzoni, 2012).

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- [8] Caprio, G. and Klingebiel, D. 1999. "Database of Systemic and Non-Systemic Bank Insolvencies from the 1980s Onwards." _World Bank._

- [9] Chang, R. 1994. "Endogenous Currency Substitution and Inflationary Finance." _Journal Money, Credit and Banking._ 26(4) pp. 903-916.

- [10] Clements, B. and Schwartz, G. 1992. "Currency Substitution: the Recent Experience of Bolivia." _IMF Working Paper._ Washington, DC.

- [11] Demirgiiu-Kunt, A. and Detragaiche, E. 1998. "The Determinants of Banking Crises in Developing and Developed Countries." _IMF Staff Papers._ 45 (1) 1998.

- [12] Fischer, S. 1982. "Seigniorage and the Case for a National Money." _Journal of Political Economy._ 90(2), pp. 295-313.

- [13] Guidotti, P. and Rodriguez, C. 1991. "Dollarization in Latin America: Gresham's Law in Reverse?" _IMF Working Paper._ Washington, DC.

- [14] Handa, J. 1988. "Substitution Among Currencies: a Preferred Habitat Hypothesis."' _International Economic Journal._ 2(2), pp. 41-61.

- [15] International Monetary Fund. _International Financial Statistics,_ IMF, Washington, D.C. Various Issues.

- [16] Levine, R, Loayza, N. and Beck, T. 1999. "Financial Intermediation and Growth: Causality and Causes." _Economic Growth Research._ World Bank.

- [17] Marquez, J. 1987. "Money Demand in Open Economies: a Currency Substitution Model for Venezuela." _Journal of International Money and Finance._ 6(2), pp. 167-78.

- [18] McKinnon, R.I. 1985. "Two Concepts of International Currency Substitution." in Conolly, M.D., and McDermott, J., eds. _The Economics of the Caribbean Basin._ New 'York, Praeger, pp. 101-13.

- [19] Miles, M. 1978. "Currency Substitution, Flexible Exchange Rates, and Monetary Independence" in Michael Bruno and Boris Pleskovic, eds. _Annual World Bank conference on development economics_ Washington DC: World Bank, 1996, pp. 29-62.

- [20] Mishkin, F. 1996. "Understanding Financial Crises: A Developing Country Perspective" _NBER Working Papers_ 5600, pp. 45.

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of payments deteriorated ahead of the 2008 elections, before recovering and then steadily deteriorating again over the next four years (IMF 2014): it was US$2.77 billion (8.3 per cent of GDP) in 2010, rose to US$4.92 billion (12.2 per cent of GDP) in 2012 and then again to US$5.84 billion (13.2 per cent of GDP) in 2013, before declining to US$3.6 billion (10.2 per cent of GDP) in 2014 (Figure 8).

Figure 8: Current account deficit as percentage of GDP, 2000––14 <!-- Start of picture text --> 4<br>2<br>0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br>-2<br>-4<br>-6<br>-8<br>-10<br>-12<br>-14<br><!-- End of picture text --> Source: Data from Bank of Ghana.

The period 2012–14 was the first time in Ghana’s history that the current account registered a double-digit deficit three years in a row, which significantly affected the country’s foreign exchange reserves. Specifically, Ghana’s net international reserves declined from a peak of US$4.4 billion in 2011 (equivalent to 3.1 months of import cover) to a low of US$950 million in September 2014 (approximately 0.6 months of import cover) (Figure 9).

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interest groups, the Cotton Advisory Board's cotton production estimate usually exceeds the estimate of the Directorate of the Cotton Development of the Ministry of Agriculture by more than 20 per cent **(Table 9).**

**Table 9:** **_Official and Trade Estimates of Cotton Production_** (thousand of 170 kg bales)

|Year|DCD (A)|CAB (B)|EICA (C)|(B) - (A)|(C) - (A)|
|---|---|---|---|---|---|
|1985-86|87.27|107.00|_i_15.50|19.73 (18.4%)|28.23 (24.4%)|
|1988-89|87.44|106.00|106.00|18.56(17.5%)|18.56(17.6%)|
|1989-90|114.22|135.75|135.75|21.53 (15.9%)|21.53 (15.9%)|
|1990-91|97.59|117.00|117.00|19.41 (16.6%)|19.41 (16.6%)|
|1991-92|97.14|119.00|120.78|21.66(18.4%)|23.64(19.6%)|
|1992-93|115.83|135.00|140.00|19.17(14.2%)|24.17(17.3%)|

DCD: Directorate of Cotton Development, CAB: Cotton Advisory Board EICA: East India Cotton Association, (): Difference in Percentage Sources: Office of the Textile Comn-iissioner, GOI (1995) etc.

Not only the annual production but also the opening stock of cotton is also often a matter of dispute. Representing the interest of cotton farmers and public traders, the CCI and the EICA tend to report higher levels of opening stock than other groups. For instance, against the Cotton Advisory Board's assumption of 2.37 million bales, the EICA estimated that there were 3.28 million bales in the opening stock of cotton in 1994/95.

Consequently, the current quota scheme has made the entire statistics of cotton demand, supply, opening stock and closing stock unreliable., unscientific and confusing. There is, moreover, no scientific way of judging whose estimates for cotton demand, supply and stock level were significantly incorrect even after the crop season is over. There is also no punishment mechanism for overestimating (or underestimating) groups that try to "cheat" policymakers. Under the current scheme, each group tries to maximize its long-term benefits by padded estimations, while making sure not to pad too much in order not to lose its credibility in the future.

# _Ad hoc and Short-sighted Decision Making_

Thus, the quota scheme has simply induced more frequent lobbying activities and made government decision-making more _ad hoc._ Under the current quota scheme, the cotton export quota is announced in the beginning of a year, but is allocated little by little within the year. However, since the government adjusts the quota level throughout the year, intereslt groups become occupied with lobbying throughout the entire year. As a result, the government is always umder lobbying pressure, which has made the export policy even 22
smaller than for other products. Instead of reducing tariffs by half, they lower tariffs by less than one fifth for textile or apparel products.

The main beneficiaries of the existing schemes are countries qualifying for the EU’s EBA scheme, which eliminates tariffs entirely. We lumped the GSP+ with EBA as the preferences are equally large. The AGOA apparel provision is almost equally generous, only leaving an average import tariff of 1.4% for apparel products, but it does not affect other textile categories.

Clearly, the biggest scope for export increases would come from enlarging the set of countries benefiting from the EU’s EBA scheme and the apparel provision under AGOA. Extending the generosity of the apparel provision also to other textile segments would help as well, but the overall export-boosting effect will be more limited, given the dominance of the apparel segment.

**Figure 7:  Ad-valorem tariffs by preference scheme for sub-Saharan African countries (2016)**

<!-- Start of picture text --> 0.12<br>0.10<br>0.08<br>0.06<br>0.04<br>0.02<br>0.00<br>EU US EU US EU US EU US<br>leather & footwear textile apparel all products<br>EBA/GSP+ (EU); AGOA-apparel (US) GSP (EU); AGOA-regular (US) No scheme (MFN)<br><!-- End of picture text --> Source: WITS, unweighted averages across HS 6-digit products (percentages).

**EU-GSP:** Cameroon, Congo, Cote d'Ivoire, Ghana, Kenya, Nigeria, Swaziland **EU-GSP+:** Cape Verde

**EU-EBA:** Angola, Benin, Burkina Faso, Burundi, Central African Republic, Chad, DRC, Djibouti, Eritrea, Ethiopia, Gambia, Guinea, Guinea-Bissau, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mozambique, Niger, Rwanda, Senegal, Sierra Leone, Sudan, Tanzania, Togo, Uganda, Zambia **EU-No scheme (MFN):** Botswana, Gabon, Mauritius, Namibia, South Africa, Zimbabwe

**US-AGOA (regular):** Angola, Burundi, Congo, Djibouti, Gabon, Guinea-Bissau, Mali, Mauritania, Togo **US-AGOA (apparel):** Burkina Faso, Benin, Botswana, Chad, Cote d'Ivoire, Cameroon, Cape Verde, Ethiopia, Ghana, Guinea, Kenya, Liberia, Lesotho, Madagascar, Mauritius, Malawi, Mozambique, Namibia, Niger, Nigeria, Rwanda, Sierra Leone, Senegal, Tanzania, Uganda, South Africa, Zambia **US-No scheme (MFN):** DRC, Central African Republic, Eritrea, Gambia, Sudan, Swaziland, Zimbabwe

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identification test is well above 10, indicating that the instruments are strong (Staiger and Stock, 1997). The Hansen’s J statistic and its P-value suggest that we cannot reject the overidentifying restrictions. Estimates from panel A suggest a significant causal impact of credit constraints on fraud victimization. Credit constraints lead to both higher probability of being fraud victims and higher subsequent economic losses.

The estimated effects of credit constraints on fraud victimization are larger for the IV specification than for the OLS specification for plausible reasons. First, the OLS estimates might be biased due to endogeneity. Credit constraints are empirically shown to be positively related to risk aversion. Guiso et al. (1996), for instance, have documented that households with background risk (such as uninsured income risk) may suppress their willingness to bear other avoidable risks. Those households may reduce exposure to risks by reducing investment in risky assets and engagement in risky activities (Cardak and Wilkins, 2009). As risk aversion is associated with lower probability of fraud victimization, the OLS baseline results likely under-estimate the true effects. Second, the IV specification identifies the local average treatment effect (Angrsit and Pischke, 2009). The sample whose credit constraints status is affected by the housing privatization reform and local bank density are those for whom the marginal effects of credit constraints are likely to be greater than those not affected by the reform and the local bank density.

# **6. Are households randomly approached?**

Are households randomly approached by fraudsters? Are being approached by fraudsters and becoming fraud victims jointly determined by common factors? When the household characteristics do not systematically differ between approached and unapproached households, selection bias is not a concern. This might be the case at earlier times when fraudsters chose targets randomly based on telephone numbers or addresses. However, with the development of internet and big data technology, individual information is easily leaked (such as by telecom operators and online shopping websites), and fraudsters could approach households strategically to increase the success ratio.

To explore the differences between the approached and the unapproached households, we first examine the determinants of being approached by fraudsters (approach propensity). Since the recent criminology literature suggests that individuals with certain characteristics 18
not richer than the poorest countries in the past, the richest countries are immensely better-off. Instead of Britain and the Netherlands that were the richest countries in 1850, today, we have USA, Singapore and Norway, all with incomes around $PPP 30,000, that is, 13 times richer than Britain or the Netherlands in 1850.<sup>11</sup> At the bottom of per capita income ―league‖, we have Congo, Burundi, Niger and Central African Republic, all with incomes just above subsistence, some $PPP 350-700 per capita, not at all different from the level of the poorest countries in 1850. The world today presents a peculiar picture where some of its parts are immensely richer than ever in history while other parts have an income level about the same as it was 150 or even 500 years ago. Thus to speak of an average global income is correct in an arithmetic sense but otherwise lacks much meaning.

Even when we contrast the fast-growing ―emerging economies‖ of China and India with the rich world, the gap in the first decade of the 21<sup>st</sup> century is greater than it was around 1850. To keep to the comparison from the 1850s, consider the GDP per capita of the United Kingdom (not any longer the richest country in the world) against GDPs per capita of India and China (among the fastest growing economies in the last two decades)—all GDPs adjusted for the differential price levels between the countries. The gap between UK and India in 2009 is in excess of 10 to 1 while it was only 5 to 1 in the mid-19<sup>th</sup> century;<sup>12</sup> the gap between UK and China is 5 to 1 today while in 1850 it was (as we have seen above) less than 4 to 1.<sup>13</sup> Indeed, the number of countries today is much greater than it was some 160 years ago, and it may seem that this factor alone would lead us to conclude that inter-country income differences, and the gap between the top and the bottom, must have widened anyway. But the widening is not an artifact of the number of countries. We can see this if we limit our observations to the same 63 countries as included by Maddison in 1870,<sup>14</sup> and whose populations 11 In 1990 international prices. We leave out some outliers, small oil-producing economies and Luxemburg, with even higher incomes.

12 Maddison does not give GDP per capita estimate for India in 1850, so we use the next proximate year (1870). It is believed that Indian GDP per capita was stagnant throughout much of the 19<sup>th</sup> century, so the exact year should make little difference.

> 13 Data from World Bank _World Development Indicators_ , year 2009, expressed in 2005 PPPs.

> 14 The number of countries included by Maddison in 1850 (twenty-four) is too small for a valid comparison.

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_Gender Equality and Economic Development: The Role for ICTs_ 3

# **1. Introduction**

It has become widely understood that promoting gender equality is an essential component of an effective economic and human development strategy. For example, World Bank (2003a) reports: “ _There is now a shared understanding within the development community that development policies and actions that fail to take gender inequality into account and fail to address disparities between males and females will have limited effectiveness and serious cost implications_ .” World Bank (2003a), p. 4 Historical data reveal that there is a statistically significant positive association between gender equality in education and economic development. Figure 1 presents the scatter plot and the linear regression line of the ratio of female to male literacy rates in year 2000 against the level of per capita GDP (PPP adjusted) in 2000. It can be seen that the ratio of female to male literacy rates tends to be positively associated with levels of GDP per capita. Similarly, Figure 2 shows that an alternative measure of gender inequality in education, the ratio of female to male students in primary and secondary schools, also tends to have a statistically significant positive association with economic development.

Further, Abu-Ghaida and Klasen (2002) provides empirical evidence indicating that a country failing to close the gender gap in education could experience a decrease in per capita income by 0.1 to 0.3 percentage points. Similarly, the _Arab Human Development Report 2002_ concludes that the low empowerment of women is one of the factors that have seriously hampered human development in the region over the last three decades (United Nations, 2002). In view of the above, issues surrounding gender equality need to be addressed for effective and sustainable economic and human development.

Given the importance of gender equality to economic development, this paper focuses on improving gender equality in education and employment using information and communications technologies (ICTs). With their ability to overcome geographical boundaries and relatively low access costs, ICTs have revolutionized the transfer of information and knowledge around the world. Enabling women to acquire education through distance learning, or allowing women to telecommute and hence participate in the labor market from their homes are just two common examples of how ICTs can help in improving gender equality. Using five different indicators of the level of the ICT infrastructure and estimating OLS and IV panel regressions with country fixed-effects, this paper presents robust results indicating that improvements in the ICT infrastructure