rafmacalaba/datause-encoder-data huggingface dataset:
# **2. Indian Establishment Data and Index Calculations**

This section begins with a description of the Indian manufacturing and services data that we use in our study. We then outline how we calculate our indices of district-level specialization and diversity and describe some of the important empirical properties of these indices. These depictions draw on the work in Ghani, Kerr and Tewari (2013), and we note in particular the deviations that occur in our present study compared to the earlier work. The earlier paper also showed the broad convergence and comparability of India’s specialization and diversity values to those in the United States for the manufacturing sector in recent periods, which we do not carry forward to this study.<sup>2</sup>

## _Indian Manufacturing Data_

We employ repeated cross-sectional surveys of manufacturing establishments carried out by the government of India for the fiscal years of 2000, 2005, and 2010. In all cases, the survey was undertaken over two fiscal years (e.g., the 2000 survey was conducted during 2000-2001), but we will only refer to the initial year for simplicity. The organized and unorganized sectors of Indian manufacturing are surveyed separately, as described next. In every period except the last one, our surveys for the two organized and unorganized sectors were undertaken contemporaneously. In the last period, we combine the 2009-2010 survey for the organized sector with the 2010-2011 survey for the unorganized sector. We will again refer to this period as 2010 for simplicity.

The organized sector comprises establishments with more than 10 workers if the establishment uses electricity. If the establishment does not use electricity, the threshold is 20 workers or more. These establishments are required to register under the India Factories Act of 1948. The unorganized manufacturing sector is, by default, comprised of establishments which fall outside the scope of the Factories Act. The organized sector accounts for over 80% of India’s manufacturing output, while the unorganized sector accounts for over 80% and 99% of Indian

> 2 Throughout this study, we use the term “sector” in two ways: to signify manufacturing and services sectors broadly defined and/or organized and unorganized establishment groups. We use the term “industry” to denote individual industries (e.g., textiles; hotels and restaurants) within these sectors.

5
existing plants. Growth opportunities do not affect a firm’s out-sourcing/in-sourcing decisions or likelihood of opening a new plant or signing new licensing agreements. On the other hand, firms with high growth opportunities are less likely to discontinue products and close plants.

All industries are not equally innovative as suggested by the F-tests reported at the foot of the table. Although not reported in the table, our results indicate that of the 26 industries that the firms in our sample belong to, those in electronics, metals and machinery, chemicals and pharmaceuticals, telecommunications, auto and auto components, and other manufacturing are the most innovative in this sample of countries.

Table 3 also shows that for this sample of countries, there is contrasting evidence on the effect of average GDP/Capita of the country on core innovation versus the other types of innovative activities a firm undertakes. The coefficient of Log GDP/Capita is negative and significant only in the case of firms introducing new product lines, but is positive and significant for firm discontinuing products, opening and closing plants, signing joint ventures and licensing agreements and outsourcing production activities.

# **_5.1. External Financing and Innovation_**

In this section, we examine whether the availability of external finance affects the extent of innovation that a firm undertakes. In the Investment Climate Assessments surveys, enterprise managers were asked: “Please identify the contribution over the last year of each of the following sources of financing for your establishment’s new investments that includes new land, buildings, machinery and equipment.” The sources are _internal funds or retained earnings_ , _local commercial banks_ (loan, overdraft), _foreign owned commercial banks, leasing arrangement_ , _investment funds_ , _trade credit_ , _credit cards_ , _equity, funds from family and friends_ , _informal sources_ , such as moneylenders, and _other_ sources. The sum of these proportions adds up to one hundred percent. The survey also asks firms if they have a bank loan and the year in which the financing was approved. Hence we construct a Bank Loan dummy which takes the value 1 if the firm had been approved for a bank loan in 2001 or earlier and 0 if the firm did not have a bank loan approved prior to 2002. The Bank Loan dummy measures past access to external financing.

15
<!-- Start of picture text --> .068<br>Daykundi<br>Wardak<br>Ghor .066<br>Sar−e−Pul Baghlan<br>Farah<br>Samangan Urozgan<br>Bamyan<br>Kunarha<br>Baghdis<br>Kapisa<br>.064<br>Takhar<br>Zabul Jawzjan<br>Faryab<br>Laghman<br>Nangarhar BalkhParwan<br>Paktika Herat .062<br>Nooristan<br>Panjsher Kunduz<br>Nimroz<br>Income Uncertainty and Narrow Wealth<br>Badakhshan Ghazni<br>Paktya Logar Variance of Log Income (log)/Income (log)<br>Kandahar<br>Helmand .06<br>Figure 3: Khost<br>Kabul<br>.058<br>12 10 8 6 4<br>Gold, Silver and Livestock (log)<br><!-- End of picture text --> 36
enhanced transparency in the procurement of PPI projects. Moreover, the Act established PICKO as a specialized agency to provide technical assistance to competent authorities on the preparation of feasibility studies and on the preparation of PPI projects tenders. More specifically, PICKO<sup>16</sup> was responsible for the review of feasibility studies prepared by competent authorities (ministries and local government), and was also in charge of the evaluation of bids. Finally, a ten-year PPI plan listing priority PPI projects was prepared by the government. The 10-year plan includes priority projects pre-identified as such and for which competent authorities have prepared pre-feasibility studies (see Annex I). Chart# 3 shows that local and central governments have been increasingly involved in developing PPI projects. These projects involved major urban infrastructures such as bridges, expressways, tunnels, and water treatment plants.

Chart# 3: Number of PPI projects approved <!-- Start of picture text --> 20 7<br>18<br>6<br>16<br>14 5<br>12<br>4<br>10<br>3<br>8<br>6 2<br>4<br>1<br>2<br>0 0<br>1995- 1997 1998 1999 2000 2001 2002 2003 2004 2005-<br>1996 04<br>No of projects awarded Investment amount US$ bill.<br>Number of project Investment amount<br><!-- End of picture text --> Source: PICKO (2005) Additionally, bank financing of PPI projects also increased tremendously. As shown in Chart# 4, bank financing of PPI projects reached a total of KRW 20,000 billion in 2002 compared to KRW 2,500 billion in 1995 when the first PPI legislation was enacted. But

> 16 In January 2005, the government of Korea passed an amendment to the 1999 Act establishing PIMAC as the new PPI unit. The amendment also introduced the following changes: (a) inclusion of social infrastructure such as schools, housings in the list of sectors eligible to PPI, (b) introduction of BTL (Build Transfer and Lease), (c) creation of an Infrastructure Fund, (d) requirement to assess the value for money for all PPI projects, (d) PICKO of KRIHS and PIMA of KDI are merged into PIMAC to be hosted by KDI.

21
# 1. **Introduction**

The origin of the “financial development” concept goes back to the early work of Gurley and Shaw (1955, 1956, 1960, 1967), who argued that financial development is a positive function of real wealth: As countries’ income and wealth grows, their financial structures tend to become more sophisticated in terms of institutions and financial assets available. _“_ During economic development, as their incomes per capita increases, countries usually experience more rapid growth in financial assets than in national wealth or national product,” they wrote (Gurley and Shaw 1967, 257). “Financial growth in excess of real growth is apparently a common phenomenon around the world over time, for any one country as its income per capita increases, financial assets rise relative to national real wealth.” The theoretical work of Gurley and Shaw found support in the quantitative study by Goldsmith (1969), who concluded that in most of the 35 countries investigated (both developing and developed), the financial ratio tends to increase with higher real income and wealth.

A powerful and influential school of thought pioneered by McKinnon (1973) and Shaw (1973) introduced financial development as a process and strategy for achieving faster economic growth. The importance attached to money and financial structure as determinant factors in economic development shifted thinking about the role of money and financial policies. Both McKinnon and Shaw claimed that liberalization from restrictions such as interest rate ceilings, high reserve requirements, and selective credit programs facilitates economic development, because when the interest rate is below equilibrium, higher rates of interest increase savings and lead to a greater efficiency of capital allocation. They argued that higher interest rates resulting from financial liberalization induce households to increase savings and stimulate financial intermediation, thereby increasing the supply of credit to the private sector, stimulating investment and economic growth. In addition, positive real interest rates lead to more efficient credit allocation, which provides an additional impact on growth.

The McKinnon‐Shaw hypothesis was influenced to a large extent by the poor development record in the 1960s of many developing countries characterized by intensive government intervention. The financial liberalization perspective exerted increasing influence over international organizations such as the International Monetary Fund (IMF) and the World Bank. Consequently, implementation of financial liberalization policies became popular in many developing countries during the last 25 years. The results of these reforms have been mixed.

The Arab Republic of Egypt is an ideal case study to examine the relationship between finance and economic growth. Its economy came under the influence of socialism between 1959 and 1972. Since 1973 it has been moving toward capitalism. These changes directly affected the structure and policies of the financial sector in Egypt over the last four decades.

2
**Table 4. Destination of Sub-Saharan African Countries' exports, 1997 (percent)**

|**Country**|**EEC**|**United**<br>**States**|**Japan**|**African**<br>**developing**<br>**countries**|**Asian**<br>**developing**<br>**countries**|**Others**|
|---|---|---|---|---|---|---|
|_West_**_and Central Africa_**|||||||
|Benin|16.9|3.2|0.6|12.6|27.3|39.6|
|Burkina Faso|30.7|0.5|2.1|30.8|23.2|12.7|
|Cameroon|73.0|0.7|0.7|8.4|12.4|4.7|
|Central African Rep.|47.5|_0.5_|0.3|10.0|3.5|38.2|
|Chad|45.2|2.8|1.6|9.7|24.3|16.3|
|Congo, Rep. Of|36.2|23.8|0.4|1.4|29.4|8.8|
|Cote d'Ivoire|52.4|6.7|0.3|25.4|4.7|10.6|
|Equatorial Guinea|37.1|10.3|15.0|10.7|26.6|0.3|
|Gabon|12.7|68.0|3.2|1.6|11.0|3.4|
|Guinea-Bissau|14.4|0.1|0.8|1.6|82.2|0.9|
|Mali|31.5|1.4|1.0|8.5|44.1|13.4|
|Niger|46.0|29.8|0.2|8.8|7.8|7.5|
|Senegal|15.8|0.2|0.3|36.6|27.8|19.3|
|Togo|14.7|2.4|0.0|22.1|31.2|29.7|
|Gambia, The|86.1|1.6|4.7|1.0|4.4|2.3|
|Ghana|49.4|8.4|4.4|17.6|8.5|11.6|
|Guinea|39.0|12.4|0.4|6.7|4.8|36.6|
|Liberia|48.0|0.4|0.0|1.3|7.3|43.0|
|Mauritania|59.9|0.1|24.5|10.9|1.8|2.8|
|Nigeria|29.0|38.1|1.1|10.5|11.1|10.1|
|Sierra Leone|69.7|8.0|0.9|4.0|0.7|16.7|
|**_East and Southern Africa_**|||||||
|Angola|14.6|64.9|0.1|1.5|15.4|3.5|
|Botswana|..|..|..||||
|Burundi|48.8|0.9|0.0|2.7|0.7|46.9|
|Congo, Dem. R.|59.5|21.4|3.7|10.2|3.4|1.7|
|Eritrea|..|..|..||||
|Ethiopia|50.8|12.0|11.2|5.8|2.5|17.7|
|Kenya<br>|34.5|3.0|0.8|40.5|9.8|11.4|
|Lesotho|..|..|..||||
|Madagascar|69.1|9.6|5.8|8.0|3.8|3.7|
|Malawi|27.9|11.8|4.5|25.1|6.3|24.4|
|Mauritius|74.0|14.3|0.6|5.7|1.8|3.6|
|Mozanbique|35.5|12.0|8.0|25.1|12.0|7.4|
|Namibia|..|..|..||||
|Rwanda|66.1|3.6|0.0|4.8|8.0|17.4|
|Somalia|13.3|0.1|0.0|1.6|2.1|83.0|
|South Africa|28.8|5.5|4.9|13.8|11.7|35.2|
|<br>Sudan|<br>35.3|<br>2.3|<br>4.2|<br>2.4|<br>11.6|44.1|
|Tanzania|33.1|3.6|7.5|16.9|28.4|10.5|
|Uganda|71.9|6.0|0.7|2.2|3.3|15.9|
|Zambia|23.1|4.4|10.7|20.8|28.9|12.1|
|Zimbabwe|31.8|5.2|6.7|37.7|8.9|9.6|
|**_Sub-Saharan Africa_**|**33.5**|**18.1**|**3.3**|**13.3**|**11.6**|20.2|

**_Source:_** IMF, Direction of trade data base and authors' calculations.

9
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28
Coimbatore is another interesting example of a successful city. It falls in the category of a Production Centers, with a population of 2.3 million and a GDP per capita of $4,132 (2015). Coimbatore is a fast‐ growing city and a major industrial hub, known for its mechanical engineering sector. The city has outperformed India in GDP per capita, employment growth, and productivity (1.3, 2, and 1.4 percentage points above the national average, respectively). To achieve its success, the city had the private sector to step up and promote the city as a thriving investment opportunity, with a highly educated workforce, leveraging the city’s skill set and reinforcing its advantage on human capital (Kulenovic and Cech, 2015). On the pollution side, vehicular and industrial activities are the main contributors to air pollution. As part of their pollution management strategy, Coimbatore participates in The Smart Cities Mission, an initiative of the Indian Ministry of Housing and Urban Affairs. In this context, the city has successfully implemented three projects on the last three years and is on the verge of implementing 5 more projects during 2018 (such as bike sharing and developing green spaces in the city).<sup>18</sup> The annual average PM 2.5 concentration level in 2015 was 27 μg/m<sup>�</sup> (still above what is considered safe by the WHO), and the city keeps making efforts to reduce those levels. For example, there are proposals to plant specific types of trees that can help absorb certain pollutants.<sup>19</sup>

> 18 Published in India Times, <u>https://timesofindia.indiatimes.com/city/coimbatore/coimbatore‐in‐top‐10‐amongst‐ best‐performing‐smart‐cities/articleshow/62575654.cms, accessed on May 2, 2018.</u>

> 19 Published in India Times, <u>https://timesofindia.indiatimes.com/city/coimbatore/these‐pollution‐eating‐trees‐can‐ cleanse‐citys‐air/articleshow/62515148.cms, accessed on May 2, 2018.</u>

26
**Are Gender Differences in Performance Innate or Socially Mediated?**<sup>**1**</sup>

Ariel BenYishay, College of William & Mary Maria Jones, World Bank Research Group Florence Kondylis, World Bank Research Group Ahmed Mushfiq Mobarak, Yale University _Keywords_ : gender, technology adoption, agriculture, discrimination _JEL Codes_ : J16, O12

> 1 Corresponding authors: Florence Kondylis <u>(fkondylis@worldbank.org)</u> and Mushfiq Mobarak (ahmed.mobarak@yale.edu). This draft benefited from comments from Manuel Bagues, Esther Duflo, David Evans, Arianna Legovini, Isaac Mbiti, David Rorhbach, Gil Shapira, Daniel Stein, and seminar participants at the 2013 World Bank Annual Meetings, IFPRI, DECRG, JPAL-Europe Labor Conference, UC-Merced and Cornell University. Siyao Zhu provided superb research assistance. Generous funding from the World Bank i2i, Gender Action Plan and Research Support budget, the Millennium Challenge Corporation, the Macmillan Center at Yale, and the Center for Business and Environment at Yale made this research possible, as well as support from the World Bank Development Impact Evaluation unit and Innovations for Poverty Action in Malawi. The views expressed in this manuscript do not reflect the views of the World Bank. All errors are our own.
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Page 16 conditions implied that dealing with banks' bad debts ! would be less costly in fiscal terms. In Hungary and Poland the total enterprises' loans as a share of GDP was less than 20 percer.t but ..! Bulgaria and Romania these were about 50 percent. This might be explained by the fact that Hungary and Poland started the banking reform several years before the political openingtook pIace. polItIcal opening took place. In particular, the size of the

|**Table S. Macroeco**|**nomic**<br>**Indic**<br>|**ators, Nor**<br>**Costs**|**perform**|**ing**<br>Loans**a**|**nd Fiscal**|
|---|---|---|---|---|---|
|_||||||
|_Ratio of Enterprises'Bank_<br>_CredittoGDPrinpercent)_||||||
|In end of 1990|25.8|<br>15.6|<br>60.5|<br>78.8|<br>38.6|
|InJune 1991|24|<br>18.4|<br>62.4|<br>47.8|<br>44.6|
|_Real InterestRates_||||||
|In end of 1990'|1.9|<br>-44.4|<br>-32.6|<br>-35.5|<br>-96.8|
|In 3rd Quarter of 1991<br> <br>|8.1|<br>15.6|<br>2.3|-71.9|<br>-31.7|
|__na_<br>_a_<br>_fhiito_<br>i<br>_AnnaaJ Rate of lrMJanon O?n_<br>_percent)_||||||
|In end of 1990|33.4|<br>250|<br>16.6|<br>64|<br>150.1|
|In June 1991|36|<br>79.9|71.3|<br>554.6|<br>224.9|
|_Memo Items:_||||______||
|Estimnated Ratio of<br>Nonperforrning to Total<br>Loans in 1991 (in percent)|50|<br>40<br>_|55|44.2|36.6|
|Estimated Fiscal Cost of<br>Removing all Bank<br>Nonperforming Loans Cin<br>percent of GDP) in June<br>1991'|5.4|6.5|5.6|17.7|<br>22.9|

Source: Countries official **statistics** and author's **estimates** I Because the end of 1990 CSFR's real interest rate was not available, I have used the **first** quarter of 1992 .

2 It is the interest cost of either swapping government bonds for bank nonperforming loans or of providing a governrnent guarantee on these loans. Since there is no market government bonds in these countries, I have used the average nominal lending rate **as a** proxy.

monetary overhang in these two countries was relatively small in early 1990, while in Bulgaria and Romania it was very high. Furthermore, Hungary, Poland and the CSFR were very effective in stabilizing their economies and in lowering the inflation rates.

The low ratio of total loans to GDP and the successful macroeconomic conditions had two very imDortant consequences First, the lower inflation rate, by lowering the nominal interest rate, reduced die fiscal cost of overcoming the enterprises' bad debt problems. For instance, had governments assumed all bank bad loans, the fiscal cost would have been about 6 percent of GDP in Hungary, Poland and the CSFR, while it would have been about 20 percent of GDP in Bulgaria and Romania.

While assuming all bad loans would not be good economic policy, it illustrates the likely effect of such