rafmacalaba/datause-encoder-data huggingface dataset:
# Table of Contents

|Inc|entives and Protection in Indian Industry|1|
|---|---|---|
|A.|Nominal and Effective Protection in Manufacturing|<br>2|
|B.|Simulating Free Trade|9|
|C.|Developments in Selected Subsectors|1 4|
||a) Iron, Steel, and Ferro-Alloys<br>b) Petrochemical Industry<br>c) Capital Goods Industries|18<br>23<br>31|
|D.|Conclusions|48|
|Ref|erences|51|
5 percent. This may suggest that our previous findings<sup>of relationshipsbetweenownershipand</sup> firm characteristics is not driven by ex-ante differences<sup>infirmperformancethat endogenously</sup> determine investor types (i.e. VCs are more likely to<sup>investin more profitablefirms). However,</sup> in Column 2 we find a positive relationship between<sup>VCownershipat20percentandROA.</sup> Column 3 and 4 show that firms with insider blockholders<sup>havesignificantly lowergross margin</sup> and again offers evidence that large VC shareholding<sup>isrelatedtohigherperformance.These</sup> findings may reflect the ex-ante decision of VCs<sup>toinvestinbetterperformingfirms.</sup> Alternatively, we have shown that large VC ownership<sup>isassociated withbettercorporate</sup> governance (such as the issuance of ESOPs and the<sup>replacementof theinsideCEO)andthese</sup> findings may also be interpreted as additional<sup>supportforpreviousfindingsofapositive</sup> relationship between better corporate governance<sup>andperformance(KlapperandLove, 2002,</sup> Black, 2001, Gompers, Ishi, and Metrick, 2001).<sup>Finally,Columns5 and6testtherelationship</sup> between access to financing and ownership. We find<sup>that firmswith largeVCinvestmentshave</sup> lower debt ratios, but this may reflect their relatively greater<sup>access to equity.</sup>

# IV. **Summary and Conclusions**

This paper examines the ownership and corporate governance<sup>structureof firmspriorto</sup> going public using a sample of Indian IPOs. We believe it is the first time that ownership structure has been analyzed just before a firm goes<sup>public.FirmsdoingIPOsareparticularly</sup> motivated to have the "appropriate" ownership structure<sup>sothat it canserveasa signalingdevice</sup> to the market. This is also the point in time of a firm's<sup>history when informationasymmetrycan</sup> be high. Firms make major realignments to their<sup>ownershipand corporategovernance structure</sup> in preparation for going public. The ownership structure<sup>just prior totheIPO can affect theIPO</sup> 17
**2.6 Contribution of land revenue to state income**

After independence, land revenue in its true sense was more or less phased out in a populist political gesture to mark a break with the colonial past. As a consequence, there has been a sharp decline in the contribution of land 'revenue' to the state's gross income. As is evident from Table 3, land revenue contributed almost onethird of Orissa's tax revenue in 1958-59. A decade later its share had fallen to seven percent, and by 1988-89 it contributed less than two percent of total revenue. In absolute terms, land revenue increased by less than one percent per year compared with an annual increase of more than nine percent in total tax revenue over the period 1958-1988. Land revenue now amounts to a cess rather than a land tax and covers only a fraction of the actual costs of land administration. In Ganjam district, for instance, land revenue amounts to around Rs. 10 million a year while the annual salaries of district revenue officials alone exceeds Rs. 100 million I<sup>1</sup> .

Stamp duty, payable on registration of land transfers, has also declined somewhat as a share of total state income over recent decades, but remains a significant source of revenue. The significance of stamp duty among the various transaction costs incurred by individuals in acquiring land through land transfer is assessed in section 4.1.2 below.

**Table 3: Changing share of state revenue from different sources** (%)

||**11958-59**|**11963-64**|**1968-69**|**11973-74**|**1978-79**|**1983-84**|**1988-89**|
|---|---|---|---|---|---|---|---|
|SalesTVax|27.3|32.8|40.4|45.4|47.5|50.9|56.5|
|Excise Duty|17.6|13.9|19.3|13.8|8.7|8.7|7.7|
|Motor Vehicles Tax|11.2|10.8|I11.9|9.8|8.7|8.4|9.5|
|Electricity Duty|0.2|5.7|9.6|8.9|14.5|15.4|17.0|
|Entertainment<br>Tax|_1.5_|1.7|1.8|1.9|2.6|2.2|1.4|
|**Other**Taxes*|0.4|1.9|1.0|4.4|3.4|2.1|0.04|
|Total Tax Revenue|100|100|100|100|100|100|100|

# Notes: Totals may not add up to 100 because of rounding.

* Other taxes include agricultural income tax, goods and passenger tax and estate duty on agricultural properties.

Source: Meher, 1993.

l S.K. Satapathy, District Collector, Ganjam, personal comm.

22
public capital in Eurasia may have been undermined weak economic policies and institutional quality associated with the presence of resource rents.

Among the various dimensions of economic policies and institutional quality, the public investment management process played a key role in defining the “actual” stock of public capital. We find that high resource rents are still associated with a low “actual” stock of public capital (Figure 9). We calculate the “actual” stock by adjusting traditional estimates of the public capital stock for the quality of the public investment management process. We use the “natural resource rent” measure from the World Bank’s adjusted net savings dataset as a proxy of resource dependence of a country. Although the statistical significance of the relationship between resource rents and the “actual” stock of public capital is relatively low, the association does point out towards an important area for future research.

**Figure 9:** _Natural resource rent_<sup>_1_</sup> _and efficiency-adjusted public capital stocks_<sup>_2_</sup> (Average: 2001-05, in percent of GDP)

<!-- Start of picture text --> 200<br>Mongolia<br>150 Afghanistan<br>Zambia<br>Albania<br>100 Thailand Mauritania<br>KAZ<br>ARM UKR Bolivia Trinidad and<br>50 Indonesia Tobago<br>Burundi Congo, Rep.<br>M OL Lao P DR Sudan Yemen Gabon<br>0<br>0 10 20 30 40 50 60 70<br>Total Natural Resource Rents<br>Efficiency Adjusted Public Capital<br><!-- End of picture text --> Sources: Author's calculation based on IMF (2012); Gupta and others (2011); and World Bank, WDI. Notes:<sup>1/</sup> Include energy (oil , natural gas, coal ), minerals and forestry. Rents of a particular commodity is defined as the difference between its world price and average extraction costs, both expressed in current US dollars. 2/Derived from Gupta and others (2011). PIMI index available for 55 low and middle-income countries only.

# **4. CONCLUSION**

This paper focuses on the case of the resource-abundant economies in Eurasia and makes three contributions to the economic literature on natural resource-led development: first, our study indicates that the stock of productive physical assets in Eurasia is relatively low, contrary to common perceptions about the Soviet system. Infrastructure assets primarily serve to meet basic human needs, but very few support the development of competitive and sustainable economies. We find that the “actual” stock of public capital created is less than half of what is suggested by traditional estimates of the capital stock using cumulative investment spending. Second, we present further evidence on the interaction between 19
Table 4: Sibling and Intergenerational Correlations: Rural Sample

||All ch|ildren|Brothers|(Men)|Sisters (|Women)|
|---|---|---|---|---|---|---|
||1993|2006|1993|2006|1993|2006|
||||PANE|L A|||
|Sibling Correlation (SC)|0.579***|0.569***|0.556***|0.571***|0.746***|0.698***|
||(72.583)|(60.826)|(47.598)|(40.090)|(57.813)|(41.992)|
|Intergenerational Correlation<br>(IGC)|0.482|0.481|0.465|0.458|0.523|0.518|
||(69.33)***|(68.16)***|(57.70)***|(52.62)***|(48.72)***|(51.02)***|
|IGC squared|0.232|0.231|0.216|0.210|0.274|0.268|
|Proportion of SC explained by<br>IGC|0.401|0.407|0.389|0.367|0.367|0.385|
|Controling for Caste and religion|||PANE|L B|||
|Sibling Correlation (SC)|0.565***|0.543***|0.543***|0.548***|0.732***|0.680***|
||(68.691)|(55.757)|(45.381)|(37.074)|(53.942)|(38.511)|
|Intergenerational Correlation<br>(IGC)|0.464|0.450|0.449|0.428|0.500|0.484|
||(65.11)***|(62.26)***|(54.03)***|(48.13)***|(45.46)***|(46.44)***|
|IGC squared|0.215|0.203|0.202|0.183|0.250|0.234|
|Proportion of SC explained by<br>IGC|0.381|0.373|0.371|0.335|0.341|0.345|
|Controling for Neighborhood FE<br>Sibling Correlation (SC)|0.379***|0.327***|PANE<br>0.361***|L C<br>0.355***|0.480***|0.396***|
||(35.604)|(25.766)|(23.351)|(18.399)|(18.073)|(12.677)|
|Intergenerational Correlation<br>(IGC)|0.414|0.400|0.414|0.398|0.449|0.430|
||(69.31)***|(62.63)***|(54.39)***|(47.67)***|(48.21)***|(45.05)***|
|IG squared|0.171|0.160|0.171|0.158|0.202|0.185|
|Proportion of SC explained by<br>IGC|0.452|0.490|0.474|0.446|0.420|0.466|
|No. of observations|22,308|20,191|14,510|12,247|7798|7,944|

Robust t statistics in parentheses. Standard errors corrected for clustering at family level * significant at 10%; ** significant at 5%; *** significant at 1% 39
# The African Financial Development and Financial Inclusion Gaps

FRANKLIN ALLEN, ELENA CARLETTI, ROBERT CULL, JUN “QJ” QIAN, LEMMA SENBET, AND PATRICIO VALENZUELA<sup>1</sup> JEL CODES: G2; O1; O16; O55.

KEY WORDS: Financial development, financial inclusion, development gaps, Africa

> 1 Allen is from Imperial Collage London, UK, Carletti is from Bocconi University, IGIER,Milan, Italy, and CEPR, London, UK,  Cull is from the World Bank, Qian is from Boston College and Shanghai Advanced Institute of Finance, Shanghai Jiao Tong University, Senbet is from University of Maryland and the African Economic Research Consortium, Nairobi, Kenya, and Valenzuela is from the Department of Industrial Engineering, University of Chile.
60

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Policy Research Working Paper 5267

# **Abstract**

This paper is about micro foundations of productivity and growth. There are several studies on productivity for advanced economies but relatively few for developing countries. Using data from the investment climate surveys of the World Bank, estimation results from 45 developing countries, complemented by extended analysis at firm and industry levels for Brazil and India for the period 2002–05, indicate the following: (i) confirmation of the importance of total factor productivity at firm, industry and national levels, but total factor productivity progressively tapers off at each level of aggregation implying that there is a less than one-to-one relationship between micro-efficiency, sector growth, and macro growth; (ii) capital accumulation is more important at the macro level than the micro level; (iii) productivity at the micro level is driven by research and development, the capacity utilization rate, and adoption of foreign technology (all of which involve management decisions), and is negatively related to corruption and instability, tax, and financial regulations; and (iii) confirmation of the lower contribution of total factor productivity to output growth in developing countries than in developed economies. Management decisions are involved in a lot of day-to-day operations at the firm level and therefore management is an unmeasured input. In developing countries, at the firm level, there is a need to understand the contribution of quality of inputs (management quality, education and labor quality, training, experience of workers, use of computers at work) and also the role of external agglomeration (for example, location in a booming city, competitive pressures from new firms, trade competition, and regulations).

This paper—a product of the Growth and Crisis Unit, World Bank Institute (WBI)—is part of a larger effort in the department to share knowledge with policy makers and practitioners for which WBI is preparing a Flagship Course on Pathways to Development. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at Rnallari@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
are commonly misused must be kept in mind at all times, especially when measures are first being developed, collected and assessed.

In light of these strands of the governance literature, this paper defines governance as the <u>combination of political, social, economic and institutional factors that affect the behavior of organizations and individuals and influence their performance.</u> While this definition is still fairly broad, it excludes two important ways in which governance is sometimes discussed. First, it excludes definitions of governance that do not identify a specific organization or individual as the unit of analysis. Under this definition, it would not make sense to talk about governance of “the health sector” because this lacks clarity regarding whose behavior is being influenced. Rather it becomes necessary to specify whether the focus is governance of private health care providers, a ministry of health or a social security institute.

The value of identifying a unit of analysis other than a country or sector is the ability to specify which relationships are likely to make a difference to the unit‟s performance and how they operate. For example, if the unit of analysis is a health insurance agency, questions regarding the authority and impact of boards of directors, regulators and even health insurance competitors are relevant (See Figure 1). By contrast, none of these relationships are likely to be relevant for a Ministry of Health which is likely to be influenced more by parliament, political parties, and the press and, if it directly provides health care services, to face competition from private health care providers.

**Figure 1: Example of Governance for a Specific Unit – a Mandatory Health Insurer**

# **<u>Accountability to:</u>**

**Beneficiaries Government Employers & other Supervisors non-beneficiary contributors Regulators**

<!-- Start of picture text --> Mandatory Health Insurer<br>Forms of Payment Competition With:<br>& Negotiation With:<br>Other mandatory health insurers<br>Providers Other insurers outside the<br>mandatory system<br>Source: Savedoff and Gottret 2008.<br><!-- End of picture text --> ______________________________________________________________________________________

-7-
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United Nations High Commissioner for Refugees (2007), _Convention and Protocol_ _Relating to the Status of Refugees_ , Geneva: UNHCR.

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Van der Geest, K., A. Vrieling and T. Dietz (2010), ‘Migration and environment in Ghana: a cross-district analysis of human mobility and vegetation dynamics’, _Environment and Urbanization_ , **22** (1), 107–24.

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57
African countries, including all those that the data will permit, but we also attempt to include a few countries from other parts of the developing world. The resulting countries for analysis are: Bangladesh, Brazil, Burundi, China, Ethiopia, India, Kenya, Malawi, Nigeria, Paraguay, Senegal, South Africa, Tanzania, and Uganda.<sup>7</sup> We also carry out the estimates for the United States, United Kingdom, Norway, and Sweden to allow for developed country comparisons.

To allow for cross-country comparisons in the estimates, we use secondary data from sources that use a common methodology to generate their numbers based on each country’s primary data. The country-specific data essential for the cost estimation is drawn from official sources including the International Labor Organization’s (ILO) Key Indicators of the Labor Market (KILM); the World Bank statistics from the World Development Indicators (WDI), HNPStats and EDStats; and the World Health Organization’s Global Health Observatory database. In some cases, we used statistics that were generated differently by country; these are indicated in the methodology discussion below.

# **a. Methodology for computing the cost of leaving school early**

Measuring the loss in potential earnings due to lower educational attainment is done in two steps, in a methodology similar to World Bank (2003) and Chaaban (2007). First, an age earnings profile is created by gender and education level. To calculate the wage for each cell, we adjust the mean national wage for a given country using coefficient estimates from latest national Mincer regressions to evaluate the variation of wages according to education, age and gender.

Second, we sum the total lifetime earnings of having a certain level of educational attainment and subtract from it the total lifetime earnings of having a lower level of education. This gives us the net gain that a country would theoretically enjoy if that girl finished the next level of education. This is done for all children and also disaggregated for males and females.

The equation used to generate the foregone earnings of having a higher degree is the following:

> 7 Due to data limitations, we are not able to include all these countries in every estimation.

6
is largely focused on manufacturing sectors despite the fact that services sectors are known to be highly labor intensive, accounting for the majority of jobs across countries. The retail and wholesale sector in India is the second largest employer (after agriculture) providing jobs to 9.4% of all workers in the country. An adverse effect of labor regulations on employment in the sector can have a significant impact on overall unemployment in the country. Second, existing studies are based on macro data which raises concerns about possible heterogeneity across data points. We find strong evidence of heterogeneity as far as the effect of labor regulation on small vs. large firms is concerned.

The rest of the paper is as follows. In section 2 we describe our data, provide descriptive statistics and discuss the structure of labor laws. In section 3 we outline the estimation strategy. Our empirical results are discussed in section 4. A summary of the main findings is stated in the concluding section.

# **2.  Data and Main Variables**

We use store level data collected by the World Bank in 2006 (Enterprise survey).<sup>2</sup> The data are a cross section of 1,948 retail stores spread over 16 states and 41 cities of India. Stores in our sample are a mix of small and large ones. The National Industrial Classification (NIC-1998, Industry Division 52) classifies retailers into those operating through established stores and the rest who usually operate from home. All stores in our sample belong to the former category.

The survey contains information on a variety of store characteristics such as annual sales, employment, availability of infrastructure, access to finance, etc. It also

> 2 The survey and methodology for data collection are available at www.entersurveys.org.

2