rafmacalaba/datause-encoder-data huggingface dataset:
establishing new plants. The analysis shows that the reform significantly increased the rate at which firms introduce new product varieties to the market. In the estimation exercise, system GMM method is applied which is the appropriate model to use in dynamic panel models and it alleviates the endogeneity problem with removing firm fixed effects. In addition to the licensing reform, India had several major economic reforms on liberalizing trade and allowing foreign ownership. The relationship between licensing reform and innovation is robust to inclusion of controls for these reforms.

The empirical evidence on the impact of licensing reform on innovation is supported with a structural model. The model is analytically tractable and it presents how distortions in firms’ operations affect their decisions to invest in R&D and pursue innovative activities. License requirements decrease firms’ profits which lead to a reduction in marginal benefit of innovation and lower product creation rates.

Innovation activities have been accepted as the engine of growth. This study shows the importance of regulatory environment to prosper in innovation activities which has an important policy implication. In order to fully benefit from investments in human capital and physical capital to achieve sustainable growth and increased welfare, a favorable investment climate is required. The inefficiencies in investment climate will leave the efforts to improve the economy and increase innovation incomplete.

# **VI. References**

Aghion, Philippe, Nicholas Bloom, Richard Blundell, Rachel Griffith, and Peter Howitt (2005), “Competition and Innovation: An Inverted U Relationship,” Quarterly Journal of Economics, 120(2): 701728.

Aghion, Philippe, Richard Blundell, Rachel Griffith, Peter Howitt, Susanne Prantl (2009), “The Effects of Entry on Incumbent Innovation and Productivity,” Review of Economics and Statistics, 91(1):20-32.

Aghion, Philippe, Robin Burgess, Stephen J. Redding, and Fabrizio Zilibotti (2008), “The Unequal Effects of Liberalization: Evidence from Dismantling the License Raj in India,” American Economic Review, 98(4): 1397-1412.

Aghion, Philippe and Peter Howitt (1992), “A Model of Growth through Creative Destruction,” Econometrica, 60: 323 - 351.

Almeida, Rita and Ana M. Fernandes (2008), “Openness and Technological Innovation in Developing Countries: Evidence from Firm-Level Surveys,” Journal of Developing Studies, 44(5): 701-727.

Alonso-Borrego, C., M. Arellano (1999), “Symmetrically Normalized Instrumental-Variable Estimation using Panel Data,” Journal of Business and Economic Statistics, 17 (1): 36–49.

Arellano, M., S. Bond (1991), “Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations,” Review of Economic Studies, 58 (2): 277–297.

Arellano, M., S. Bond (1998), “Dynamic Panel Data Estimation Using DPD98 for Gauss: a Guide for Users,” Mimeo. CEMFI. ftp://ftp.cemfi.es/pdf/papers/ma/dpd98.pdf.

17
WPS3945 .39 among non-migrant households). Of course since enrollment rates for boys are much higher to being with, one might argue that raising the enrollment rates for girls should be easier. Even in this respect, though, the gains for girls are larger. The increase in enrollment closes the gap relative to 100% enrollment by 29% for girls, compared to 19% for boys.

The decline in dropout rates is also substantially larger for girls. The dropout rate for girls falls by 55% (from .56 to .25), while it declines by 44% for boys (from .25 to .14). As a consequence the gender gap in dropout rates falls sharply (from .31 to .11). Once again this implies that for girls the gap from 0 dropout rates is closed by 70% while for boys this gap is closed by 44%.

Turning next to accumulated years of schooling, unconditional on past or current enrollment, the gender differential is rather small. Girls in migrant households have about 1.5 more years of schooling compared to their counterparts in non-migrant households in the same village, while boys have about a grade more. Signi fi cantly though, prospects of migration in the future do not appear to exercise any effect on years of schooling for either boys or girls. (Table 3, speci fi cation 6).

When we look at accumulated schooling, conditional on having attended school at some point (table 3, speci fi cation 7), we get large differences in outcomes for girls and boys. In fact, comparing children in migrant households, we fi nd that girls actually exceed boys in absolute terms, completing about a fi fth of a grade more. The increase in accumulated schooling for girls is also larger than for boys if we compare children in migrant households with those in non-migrant households. While girls in migrant households complete almost 2 grades more than girls in non-migrants households, boys complete a little less than a grade more than boys in non-migrant households. The net effect is that in non-migrant households, boys are almost a full grade ahead of girls in their age cohort, while in migrant households, girls more than make up the gap, exceeding boys in their age cohort by .2 grades. Restricting the sample further by conditioning on current enrollment (table 3, speci fi cation 8) strengthens these effects further.

Since there is no gender difference in the average age in fi rst grade, our evidence suggests either a disproportionate bene fi t to girls from improved household capacity to bear income risk or a higher incidence of migration generated burdens on boys. The latter is certainly plausible in the context of rural Pakistan, where migration induced male absence may imply greater reliance on boys for any number of activities that women and girls cannot undertake due to seclusion related restrictions on their mobility. We turn 17
Policy Research Working Paper 7065

# **Abstract**

This paper examines how financial development influences foreign direct investment. The direct and indirect sectorspecific effects that source countries’ financial development and destination countries’ financial development can have on foreign direct investment are first identified in a conceptual framework. The presence and relative strength of these various channels of influence at the different margins of foreign direct investment are then empirically investigated using unique and underexploited sector-specific bilateral panel data on greenfield foreign direct investment over the period 2003–2006. Causality is established by applying a difference-in-differences approach that exploits - the variation in financial vulnerability across manufactur ing sectors. The overall effects of higher source countries’ financial development and destination countries’ financial development on the relative volume of bilateral foreign direct investment in financially vulnerable sectors are large, positive, and complementary. These effects appear to operate mainly at the intensive margin rather than at the extensive margin of foreign direct investment. There is also evidence of direct and indirect effects of financial development. The key findings are robust to the use of data on the number of bilateral Mergers\&Acquisitions transactions. Overall, the empirical results unambiguously indicate that a sophisticated and well-functioning financial system in source and destination countries greatly facilitates the international expansion of firms through foreign direct investment, especially in financially vulnerable sectors.

This paper is a product of the Trade and International Integration Team, Development Research 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://econ.worldbank.org. The authors may be contacted at rodolphe.desbordes@strath.ac.uk.

_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
listed to GDP is 0.29, ranging from a value very close to zero to 2.68. We chose to scale credit listed by GDP instead of the outstanding volume of credit the country since this yields more observations. One caveat with this variable is that reliable information on volume of credit listed in the credit bureau is only available for 55 out of 104 countries that have a credit bureau. We therefore use an alternative measure of credit bureau coverage, as reported by the Doing Business team and defined as: the total number of individuals plus firms listed in the credit bureau divided by the adult population of a country. For consistency, we also calculate this Doing Business measure of credit bureau coverage as the 2005 through 2010 average. As shown in table 1, this variable is available for 102 countries and has an average of 0.347.

Figure 2 compares both measures of credit bureau coverage across country income groups. The Doing Business measure indicates higher coverage in middle and high income countries compared to the credit listed measure. In low income countries, on the other hand, the ratio of credit listed to GDP is 0.41 while the coverage of individuals plus firms divided by the adult population is only 0.02. This large discrepancy suggests that in low income countries a small fraction of individuals or firms hold very large loan volumes.

We also constructed indices measuring (i) the extent of information distributed on each borrower by the credit bureau and (ii) the transparency of the credit bureau. The _information index_ is constructed as follows: the Doing Business survey asks a series of questions capturing what type of information credit bureaus distribute on borrowers, loans, and the repayment history of borrowers. The questions have a yes (=1) or no (=0) response. The index is constructed by adding all the questions with an affirmative response and dividing by the number of total questions. The summary statistics in table 1 show that the values of the index range from 0 to 0.89, with an average of 0.424. The index is available for 94 countries.

With respect to the information reported by credit bureaus, we use another variable that indicates whether the bureau shares positive information. The exchange of positive information is particularly costly for banks since it allows borrowers to establish reputational collateral and to access credit outside established lending relationships. From the perspective of the borrower, 9
|Table 3. Granger Causality Test, 1970-1994: I|s Import Capacity Binding?<br>|
|---|---|
|<br>________________e<br>_______________|<br>i-Statistic|
|Sub-SOPMAfIca^||
|Imports do not Granger-cause reserve positions|0.61<br>0.70|
|Reserve positions do not Granger-cause imports|3.12<br>0.(7b|
|_Eas-t_Asi||
|Imports do not Granger-cause reserve positions|1.35<br>0.33|
|Reserve positions do not Granger-cause imports|0.54<br>0.74|
|_S o_<br>_sia_||
|Imports do not Granger-cause reserve positions|5.28<br>0.02c|
|(up to 1992)|2.41<br>0.14|
|Reserve positions do not Granger-cause imports|4.03<br>**0.03c**|
|(up to 1992)|5.19<br>0.03c|
|;Eu<br>4aid rewaAsia> >-<br>_5s/_<br>_-;_||
|Imports do not Granger-cause reserve positions|0.41<br>0.83|
|Reserve positions do not Granger-cause imports|2.63<br>0.09b|
|Mid&otast AiidNorth_Aff._||
|Imports do not Granger-cause reserve positions|2.21<br>0.14|
|(up to 1992)|1.44<br>0.32|
|Reserve positions do not Granger-cause imports|1.83<br>0.20|
|(up to 1992)|2.52<br>0.13|
|-La<br>fic and the Caibbe4-||
|Imports do not Granger-cause reserve positions|1.69<br>0.23|
|Reserve positions do not Granger-cause imports|5.41<br>0.01'|

Note: Sample period: 1970-1994; number of observations: 20; number<sup>of lags used: 5.</sup>

- a. Granger causality measures precedence and information content, but does not<sup>by itself indicate</sup> causality in the more common use of the term. Y is said to be Granger-caused by<sup>X if X helps in</sup> the prediction of Y, or equivalently, if the coefficients on the lagged Xs are<sup>statistically significant</sup> in the prediction of Y with the presence of lagged Ys in the information set.<sup>If F-statistic exceeds a</sup> critical level, at least one of the coefficients attached to lagged Xs is<sup>probably non-zero, suggesting</sup> the existence of Granger causality. Normally, a probability lower<sup>than 0.05 is taken as strong</sup> evidence of rejection of the hypothesis 'X does not Granger-Cause Y,' suggesting that 'X does Granger-Cause Y.'

- b. Null hypothesis of 'X does not Granger-cause Y' is rejected at 10<sup>percent confidence level.</sup>

- c. Null hypothesis of 'X does not Granger-cause Y' is rejected<sup>at 5 percent confidence level.</sup>

17
# **Annex IIC: Stamp Duty Rates for States and Union Territories FY81-FY2003**

|**Year**|**M**|**eghalaya**|||**Nagalan**|**d**||**Orissa**|||**Punjab**||**R**|**ajasthan**||**T**|**amilnadu**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Basic**|**SC**|**Total**|**Basic**|**SC**|**Total**|**Basic**|**SC**<sup>**7**</sup>|**Total**|**Basic**|**SC**|**Total**|**Basic**|**SC**|**Total**|**Basic**|**SC**|**Total**|
|**1980-81**||||||||9|9|10|0|10|17|0|17|6|5|11|
|**1981-82**|6.6|0|6.6|||||9|9|12.5|0|12.5|17|0|17|6|5|11|
|**1982-83**|6.6|0|6.6|||||9|9|12.5|0|12.5|17|0|17|6|5|11|
|**1983-84**|6.6|0|6.6|||||9|9|12.5|0|12.5|17|0|17|6|5|11|
|**1984-85**|6.6|0|6.6|||||9|9|12.5|0|12.5|17|0|17|6|5|11|
|**1985-86**|6.6|0|6.6|||||10.5|10.5|12.5|0|12.5|17|0|17|6|5|11|
|**1986-87**|6.6|0|6.6|||||10.5|10.5|12.5|0|12.5|17|0|17|6|5|11|
|**1987-88**|6.6|0|6.6||||4.2|10.5|14.7|12.5|0|12.5|17|0|17|6|5|11|
|**1988-89**|6.6|0|6.6||||4.2|10.5|14.7|12.5|0|12.5|17|0|17|6|5|11|
|**1989-90**|6.6|0|6.6||||4.2|10.5|14.7|12.5|0|12.5||0|0|6|5|11|
|**1990-91**|6.6|0|6.6||||4.2|10.5|14.7|12.5|0|12.5||0|0|6|5|11|
|**1991-92**|6.6|0|6.6||||4.2|10.5|14.7|12.5|0|12.5||0|0|6|5|11|
|**1992-93**|6.6|0|6.6||||4.2|10.5|14.7|12.5|0|12.5|10|0|10|6|5|11|
|**1993-94**|9.9|0|9.9||||4.2|10.5|14.7|12.5|0|12.5|10|0|10|6|5|11|
|**1994-95**|9.9|0|9.9||||4.2|10.5|14.7|12.6|0|12.6|10|0|10|6|5|11|
|**1995-96**|9.9|0|9.9||||4.2|10.5|14.7|6|0|6|10|0|10|6|5|11|
|**1996-97**|9.9|0|9.9||||4.2|10.5|14.7|6|0|6|10|0|10|6|5|11|
|**1997-98**|9.9|0|9.9||||4.2|10.5|14.7|6|0|6|10|0|10|6|5|11|
|**1998-99**|9.9|0|9.9||||4.2|10.5|14.7|6|0|6|10|0|10|6|5|11|
|**1999-2000**|9.9|0|9.9||||4.2|10.5|14.7|6|0|6|10|0|10|6|5|11|
|**2000-01**|9.9|0|9.9||||4.2|10.5|14.7|6|0|6|10|0|10|8|5|13|
|**2001-02**|9.9|0|9.9||||4.2|10.5|14.7|6|0|6|11|0|11|8|5|13|
|**2002-03**|9.9|0|9.9||||4.2|10.5|14.7|6|0|6|11|0|11|8|5|13|
|**2003-04**|9.9|0|9.9||||4.2|10.5|14.7|6|0|6|11|0|11|8|5|13|

7. These rates of additional stamp duty are for property situated in urban areas. The rates of additional stamp duty situated in rural areas was 6.5% till 1984-85 and 6% thereafter.

68
# **INTRODUCTION**

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Inclusive financial systems—allowing broad access to financial services, without price or nonprice barriers to their use—are especially likely to benefit poor people and other disadvantaged groups. Without inclusive financial systems, poor people must rely on their own limited savings to invest in their education or become entrepreneurs—and small enterprises must rely on their limited earnings to pursue promising growth opportunities. This can contribute to persistent income inequality and slower economic growth.<sup>1</sup> Until now little had been known about the global reach of the financial sector—the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This report presents the first round of the Global Findex database, a new set of indicators that measure how adults in 148 economies save, borrow, make payments, and manage risk. The indicators are constructed with survey data from interviews with more than 150,000 nationally representative and randomly selected adults age 15 and above in those 148 economies during the 2011 calendar year.<sup>2</sup> The Global Findex data show sharp disparities in the use of financial services between high-income and developing economies and across individual characteristics. The share of adults in high-income economies with an account at a formal financial institution is more than twice that in developing economies. And around the world, men and more educated, wealthier, and older adults make greater use of formal financial services.

Novel cross-country data on self-reported reasons for not having a formal account make it possible to identify barriers to financial inclusion. Moreover, the ability to disaggregate data by individual characteristics allows researchers and policy makers to identify population groups that are excluded from the formal financial system and better understand what characteristics are associated with certain financial behaviors.

As the first public database of indicators that consistently measure people’s use of financial products across economies and over time, the Global Findex database fills a big gap in the financial inclusion data landscape. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world save, borrow, make payments, and manage risk. The main indicators on the use of formal accounts and formal credit will be collected yearly, and the full set of indicators every three years.

**1**

MEASURING FINANCIAL INCLUSION
<!-- Start of picture text --> Average Prices, Bread II<br>(December 1991 -- August 1992)<br>14-<br>12-;..,<br>100......................................................................... ....................<br>4d-- -- ---- ----- -- ------------- ..........................................<br>6 - ---.----- ................<br>------------------<br>t2; ---------------------------------------------------------<br>Dec91 01128 02/11 02/25 03J31 04114' 04628 05112 06102 4616' 0613007/14 07128 0811 MO5<br>Date<br>.... State Ptice<br><!-- End of picture text --> 53
Table 5: Impact on Remittance Behavior

||(1)|(2)|(3)|(4)<br>(5)|
|---|---|---|---|---|
||Receives<br>Remittance||||
||through Informal<br>Channels|Has Received<br>Remittance|Remittance<br>Frequency|Remittance: Log<br>(Amount)<br>Remittance: Level<br>(USD)|
|Migrant-Only Training (A)|-0.010|0.000|-0.497|-0.188<br>-130.906|
||(0.009)|(0.053)|(0.690)|(0.871)<br>(88.315)|
|Family-Only Training (B)|-0.010|-0.069|-0.989|-1.221<br>-127.331|
||(0.009)|(0.055)|(0.678)|(0.908)<br>(90.874)|
|Migrant and Family Training (C)|-0.014|-0.030|-0.772|-0.517<br>-58.785|
||(0.008)|(0.056)|(0.690)|(0.922)<br>(92.528)|
|R-squared|0.005|0.073|0.042|0.080<br>0.103|
|Sample Size|1131|1132|1131|1131<br>1130|
|Number of Clusters|394|394|394|394<br>394|
|Mean of Dependent Variable in Control Group|0.014|0.582|3.268|9.501<br>599.797|
|**Test: A**-**B**=**0 (p**-**value)**|0.986|0.196|0.409|0.239<br>0.966|
|Test: A-C = 0 (p-value)|0.319|0.578|0.652|0.712<br>0.402|
|Test: B-C = 0 (p-value)|0.316|0.491|0.716|0.448<br>0.439|
|Test: A = B = C = 0(p-value)|0.196|0.540|0.508|0.547<br>0.410|

This table presents pooled regression analysis across three follow-up survey rounds for measures of remittance behavior. All regressions include survey round dummies. Standard errors in parentheses, clustered at the individual level. The bottom half of the table presents p-values for equality of coefficient tests. Statistically significant coefficients are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
people without any education. The opposite is true for agriculture, though the negative effect of primary education is not significant for agriculture. Having an education level anywhere above primary school, however, makes one much less likely to participate in agriculture. This shows that education is more rewarded in non-farm activities even in rural areas.

Non-labor income, which is defined as it was for Ghana-the sum of income from rent, remittances and other income-is negative and significant for both years in the agriculture equation, but interestingly the effect is also negative for non-farm activities in the 1992 analysis. The effect is not significant in the non-farm equation for the 1996 survey, but it is still negative. This may be interpreted as an indication that neither farning nor non-farm households had access to sufficient "other" income to make a difference to their job participation decision.

The dependency ratio has the expected effect in 1992 when the interaction for gender is included (Model 2), but in 1996, it is insignificant. In 1992, the effect of the dependency ratio when men and women are together leads to higher participation in agriculture and lower participation in non-farm. When you include the interaction with the female dummy, the effect for the interaction term is the opposite. The higher the dependency ratio when the individual is a woman, the more likely she will participate in agriculture and less likely she will participate in agriculture. It is not clear why the samne relation would not hold by 1996.

Living in the Central region has a significant and negative effect on the probability of participating in agriculture and a significantly positive effect on participating in non29