rafmacalaba/datause-encoder-data huggingface dataset:
indicators are considered.

Compared to the CPI, the WGI CC, which is published by the World Bank Group, is a broader measure of public sector corruption. It measures both political and general bureaucratic corruption.<sup>8</sup> The WGI CC is updated yearly and it ranges from -2.5 (most corrupt/least effective) to 2.5 (least corrupt/most effective). It is based on 30 individual data sources, selected to include the views of citizens, business owners, academics, and experts drawn from the public, private, and NGO sectors from across the globe. Then, the WGI GE measures non-elected public sector corruption. To the extent that bureaucrats behave somewhat independently from elected officials, the WGI GE captures corruption in the bureaucracy, rather than political corruption.

Lastly, we consider one objective indicator of corruption: the index issued by ICRG. It refers to the financial corruption associated with conducting business (e.g., bribes) as well as other forms of political corruption (e.g., excessive patronage, nepotism, and close ties between politics and business).<sup>9</sup> As these four indicators use different scales, we have re-scaled them to range from 0 (least corrupt) to 10 (most corrupt). Summary statistics are presented in Table 1.<sup>10</sup> As shown in Figure 1, there is a substantial difference in corruption levels across European countries. The southeastern region of the European continent is characterized by more corruption levels (Bulgaria and Romania at 5.6, Greece at 5.4, and Italy at 5.1) when compared to the Nordic region (Finland at 0.7, Denmark at 0.8, and Sweden at 1.0).

# **2.2 Migration**

We focus on bilateral migration flow data across EU28 and EFTA countries between 2008 and 2018. These data are from Eurostat. Across the sample, countries differ both in terms of inflows and outflows of migrants, as shown in Figure 2. Larger migration outflows are observed in Eastern (Romania and Poland), Central (Germany and France), and Southern (Italy, Spain, and Portugal) European countries. The countries with the largest inflows are the U.K, Spain, Italy, and Switzerland.<sup>11</sup>

> as well as business regulation and the public perceptions of corruption (Treisman 2007).

> 8Both WGI CC and WGI GE are part of a larger set of Worldwide Governance Indicators, which include: _i_ ) Voice and Accountability; _ii_ ) Political Stability and Absence of Violence / Terrorism; _iii_ ) Regulatory Quality; and, _iv_ ) Rule of Law. See the World Bank.

> 9Besides the corruption index, there are 11 other indicators within the ICRG’s Political Risk classification. See the International Country Risk Guide.

> 10There is substantial variation in corruption levels both cross-sectionally and over time for year fixed effects to be included in the gravity model.

> 11When looking at average migration inflows, a few large countries (including Germany, Greece, Poland, and Portugal) have missing data from Eurostat.

5
come from UN Comtrade (accessed via WITS), capital stock, total factor productivity and consumption from the Penn World Tables, and arable land from the World Development Indicators (WDI). See the Data Appendix for details.

Table 1 reports the summary statistics for endowments of capital, skilled and unskilled labor, arable land (unadjusted for productivity differences) and total factor productivity in our sample over time (Panel A). Panel B and C report the same statistics for two subgroups: countries with high skilled-unskilled ratio and those with low ratio in 2000. We split the sample into two subgroups by skilled-unskilled labor ratio using the median for this ratio in 2000.<sup>1</sup> The number of countries for which data are available has gradually increased from 45 in 1975 to 107 by 2000, and arguably data for more unskilled labor abundant countries became available in later years. Yet the summary statistics shows that the average number of skilled labor stock exploded over this period. Keeping in mind changing set of countries over the years, the skilled labor endowment for an average country has increased from 7.3 to 25 million, while the unskilled labor endowment of an average country hovered around 30 million. Similar pattern albeit less striking is observed for capital endowment which for an average country stood at 0.9 and 2.2 million US dollars in 1975 and 2010, respectively. Comparing Panel B and C, we see that even through relatively high skilled labor abundant countries tend to have on average larger endowment of skilled labor and smaller endowment of unskilled labor (by construction), much of the changes in skilled labor endowment over time was driven by the group of relatively low skilled labor abundant countries. Skilled labor endowment for an average country in the later group increased from 2.1 million in 1975 to 14 million in 2010, compared to same statistic for the group of relatively high skilled labor abundant countries which only changed from 10 million to 24 million.

Table 2 summarizes sectoral net exports in million US dollars and as a share of GDP in 1975 and 2010. There is significant variation in the sectoral trade patterns within industries

> 1We have chosen year 2000 because of data availability. We have also ranked countries by skilled-unskilled labor ratio in each of the available years and the rank correlation across different years is above 0.98.

9
Table 2: F-test of joint significance of treatment arm means (H0: S0 = S1 = S2) **Variable Control Political Economic F-stat** xx̅ xx̅ xx̅

|**Variable**|**Control**<br>**Group**<br>**(S0)**|**Political**<br>**Stabilization**<br>**(S1)**|**Economic**<br>**Stabilization**<br>**(S2)**|**F-stat**|**P-value**|
|---|---|---|---|---|---|
|Respondent is male|0.383|0.389|0.394|0.088|0.916|
|Age of respondent (years)|31.015|30.776|30.853|0.356|0.700|
|Respondent is married|0.400|0.366|0.386|1.030|0.357|
|Respondent has children|0.320|0.260|0.287|3.596|0.028|
|After 11 February 2024|0.771|0.779|0.762|0.300|0.741|
|Willing to migrate|0.521|0.511|0.526|0.187|0.829|
|Individual income (USD)<br>|117|116|121|0.914|0.401|
|No. of confict related events in<br>township during prior year (ACLED)<br>|33|34|32|0.424|0.655|
|No. of confict related events in<br>township since Feb'21 (ACLED)|162|169|163|0.262|0.769|
|Overall risk appetite (ordinal, 1-10)|6.002|6.022|6.091|0.220|0.803|
|No. of employment shocks<br>experienced|1.036|1.076|0.954|1.614|0.199|
|No. of contacts abroad who can help<br>fnd a job|1.400|1.410|1.437|0.031|0.970|
|No. of contacts in Myanmar who can<br>help fnd a job abroad|1.371|1.334|1.714|2.947|0.053|
|No. of contacts abroad who may be<br>able to host short term|1.030|1.017|1.049|0.034|0.967|
|Received remitances|0.152|0.161|0.165|0.281|0.755|
|Self-employed (no hired workers)|0.208|0.209|0.210|0.007|0.993|
|Employer (hires workers)|0.113|0.107|0.130|1.087|0.338|
|Paid apprentice/intern|0.016|0.022|0.014|0.782|0.457|
|Employee|0.664|0.662|0.646|0.349|0.705|
|PhD degree|-|0.001|-|0.974|0.378|
|Master’s degree|0.013|0.007|0.018|1.878|0.153|
|<br>Postgraduate diploma|0.006|0.009|0.007|0.208|0.812|
|Bachelor’s degree|0.936|0.937|0.941|0.098|0.907|
|Undergraduate diploma|0.015|0.020|0.010|1.125|0.325|
|Dailysurveyresponse rate|0.264|0.270|0.268|0.889|0.411|

# 5. Results

## 5.1 Effects of Stabilization on Migration Intentions

current occupation under scenarios of political and economic stabilization, with and without controls. These estimates are relative to the reference category representing a status quo marked by considerable economic and political instability in the country. Across the three outcome measures of interest, we see that only political stabilization will reduce the value attached to 13
Escobal, J., J. Saavedra, P. Suárez, S. Huttly, M. Penny, C. Lanata, and E. Villar. 2005. “The Interaction of Public Assets, Private Assets and Community Characteristics and its Effect on Early Childhood Height-for-Age in Peru.” Young Lives: An International Study of Childhood Poverty Working Paper 14. Lima, Peru.

Escobal, J and M. Torero. 2002. “Asset complementarities: The case of rural Peru.” Paper presented at the 2001 Global Development Network (GDN) Annual Conference, Rio de Janeiro; Lima, Peru.

Fan, S., P. Hazell, and S. Thorat. 2000. “Government Spending, Growth and Poverty in Rural India.” _American Journal of Agricultural Economics_ 82 (4): 1038–51.

Filmer D. 2003. “The incidence of public expenditures on health and education” Background note for the World Development Report 2004: Making services work for the poor. World Bank; Washington, DC.

Filmer, D. and L. Pritchett. 1999. “The impact of public spending on health: does money matter?” _Social Science & Medicine_ 49: 1309–23.

Filmer D, J. Hammer, and L. Pritchett. 2000. “Weak links in the chain: a diagnosis of health policy in poor countries.” _World Bank Research Observer_ 15 (2): 199–224.

Glewwe, P., H.G. Jacoby, and E.M. King. 2001. “Early childhood nutrition and academic achievement: a longitudinal analysis.” _Journal of Public Economics_ 81: 345–368. Glewwe, P., and H.G. Jacoby. 1995. “An Economic Analysis of Delayed Primary School Enrollment in a Low Income Country: The Role of Early Childhood Nutrition.” _The Review of Economics and Statistics_ 77 (1): 156–69.

Gupta, S., M. Verhoeven, and E.R. Tiongson. 2003. “Public spending on health care and the poor.” _Health Economics_ 12: 685–96.

24
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|________________<br> <br>Table 10: Exporter|________<br> <br>producti|_____<br> <br>vity p|___________<br> <br>remia (perce|______________<br> <br> <br>ntage) I: Export|______________<br> <br>er dummy|___________<br>|________<br>|___________<br>|______________________________<br>|
|---|---|---|---|---|---|---|---|---|---|
|Country<br>__________________|Model<br>_________|<br>_____|Sales/work<br>_____________|er<br>p-value<br>_______________|VA/worker<br>_______________|p-value<br>_____________|TFP<br>_________|p-value<br>____________|N / NxT<br>___________________|
|Belgium|Pooled|ß|60.2|[0.00]|8.9|[0.00]|-0.7|[0.31]|4707|
|1996-2005|FE|ß|10.1|[0.00]|1.1|[0.07]|2.2|[0.21]|28426|
|China|Pooled|ß|11.7|[0.00]|1.3|[0.00]|-2.4|[0.00]|351,501|
|1998 - 2005|FE|ß|8.8|[0.00]|6.6|[0.00]|5.2|[0.00]|1,138,350|
|Colombia|Pooled|ß|23.7|[0.00]|23.7|[0.00]|1.9|[0.00]|5,837|
|1981 - 1991|FE|ß|12.9|[0.00]|11.7|[0.00]|9.5|[0.00]|44,425|
|France|Pooled|ß|19.8|[0.00]|5.0|[0.00]|1.9|[0.00]|41,230|
|1990 - 2004|FE|ß|7.4|[0.00]|2.6|[0.00]|2.1|[0.00]|293,196|
|Republic of Ireland|Pooled|ß|13.7|[0.00]|8.8|[0.00]|||3,640|
|1991 – 2004|FE|ß|6.8|[0.00]|4.1|[0.00]|||26,472|
|Italy|Pooled|ß|38.4|[0.00]|10.1|[0.00]|5.6|[0.00]|37,443|
|1989 - 1997|FE|ß|3.2|[0.00]|0.7|[0.00]|0.3|[0.10]|169,778|
|Slovenia|Pooled|ß|25.6|[0.00]|9.6|[0.00]<br>|-1.5|[0.32]|1,519|
|1994 - 2002|FE|ß|4.3|[0.01]|5.0|[0.00]|2.9|[0.22]|9,807|
|UK|Pooled|ß|10.4|[0.00]|5.3|[0.00]|-0.6|[0.37]|8,411|
|1995 - 2004<br>__________________|FE<br>_________|ß<br>_____|5.5<br>_____________|[0.00]<br>_______________|2.0<br>_______________|[0.00]<br>_____________|0.4<br>_________|[0.68]<br>____________|4,4475<br>___________________|

Note: Results are for firms from ISIC industries 15 – 36 with at least 20 employees at the median over the years covered in the panel. The firms with the bottom / top one percent of productivity in a year are excluded from all computations. Firms are classified into size classes according to the median of the number of employees over the years covered. ß is the estimated regression coefficient from an OLS-regression of log (productivity) on a dummy variable for exporting firms, controlling for the log of the number of employees and its squared value, the log of wages and salaries per employee, and a full set of interaction terms of 4digit industry-dummies and year dummies; the fixed effects model adds firm fixed effects. To facilitate interpretation the estimated coefficients for the exporter dummy variable has been transformed by 100(exp(ß)-1). p is the prob-value. N ist the number of firms, NxT is the number of observations.

54
**Grand Total** 117.30 (3.0%) 259.77 (6.7%) 377.06 (9.7%) _Source: Wilson et al., 2004._

Table 9 summarizes the change in trade flow by region, by trade facilitation indicators, and by own vs. trading partners' reforms. These results can be combined in several ways to give different perspectives on which regions gain the most and why. One cut, exports by region and by trade facilitation indicator, is shown in Figure 10.

# **TABLE 9: DETAILS OF SIMULATION RESULTS**

||**Expe**<br>|**rience o**<br> <br>|**f Expor**<br>Port efficienc<br>|**ters**<br>y<br>|Custo<br>ms<br>Environment<br>|Regu<br>|latory Enviro<br>|nment<br>|Service<br>|sector Infra<br>|structure<br>|Combined<br>effect<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Region|Initial|Importer|Exporter|Total|Total|Importer|Exporter|Total|Importer|Exporter|Total|Total|
||Trade|change,<br>percent|change,<br>percent|change,<br>Percent|Change,<br>percent|change,<br>percent|change,<br>percent|change,<br>Percent|change,<br>percent|change,<br>percent|change,<br>Percent|change,<br>Percent|
|East Asia|753|0.5|7.0|7.6|0.8|0.6|3.3|3.9|0.9|10.8|11.7|24.0|
|ECA|139|0.8|8.7|9.5|0.9|0.7|5.5|6.1|1.4|12.1|13.5|30.0|
|LAC|179|0.6|7.3|7.9|0.9|0.8|3.6|4.4|0.8|6.0|6.8|20.0|
|MENA|26|0.4|0.2|0.6|0.7|0.5|0.1|0.6|0.7|0.7|1.4|3.3|
|OECD|2,735|0.6|0.0|0.7|0.8|0.6|0.6|1.3|1.0|0.0|1.0|3.8|
|South Asia|36|0.4|11.7|12.1|0.8|0.5|6.9|7.4|0.7|1<br>9.2|20.<br>0|40.3|
|SS Africa|12|0.4|1.1|1.4|0.6|0.5|2.8|3.3|0.8|4.8|5.6|10.<br>9|
|TOTAL|3<br>879|0.6|2.2|2.8|0.8|0.6|1.5|2.1|0.9|3.0|4.0|9.7|
||,<br>**E**<br>**e**<br>**xp**|<br>**rie**<br>**o**<br>**nce**|<br>**f Im**<br>**r**<br>**po**|<br>**ter**<br>**s**|<br>C<br> <br>t|||||||<br>C<br>d<br>bi|
||||Port<br>efficienc|y|u<br>s<br>som<br>Environment|Regu|latory<br>iro<br>Env|nme<br>nt|Serv<br>ice|sect<br>ra<br>or Inf|struc<br>ture|o<br>e<br>mn<br>effect|
|Region|Initial<br>Trade|Importer<br>change,<br>percent|Exporter<br>change,<br>percent|Total<br>change,<br>Percent|Total<br>Change,<br>percent|Importer<br>change,<br>percent|Exporter<br>change,<br>percent|Total<br>change,<br>Percent|Importer<br>change,<br>percent|Exporter<br>change,<br>percent|Total<br>change,<br>Percent|Total<br>change,<br>Percent|
|East Asia|620|1.5|2.7|4.2|2.2|1.1|2.1|3.3|2.7|4.4|7.0|16.7|
|ECA|165|3.1|1.8|4.9|3.2|2.7|1.3|4|5.3|2.4|7.7|19.8|
|LAC|260|2.9|1.3|4.2|3.4|2.4|1.4|3.8|2.9|1.8|4.7|16.1|
|MENA|32|0.2|1.0|1.3|1.3|0.1|1.1|1.2|0.7|2.1|2.8|6.6|
|OECD|2,761|0.0|2.2|2.2|0.1|0.2|1.4|1<br>.6|0.1|2.9|3.0|6.9|
|South Asia|21|3.1|1.4|4.5|5.8|3.3|1.5|4.8|6.8|2.5|9.3|24.4|
|SS Africa|20|1.5|1.5|3.0|3.0|1.8|1.3|3.1|3.5|2.6|6.1|15.<br>2|
|TOTAL|3,879|0.6|2.2|2.8|0.8|0.6|1.5|2.1|0.9|3.0|4.0|9.7|

ECA: East Europe and Central Asia LAC: Latin America and the Caribbean MENA: Middle East and North Africa SS Africa: Sub-Sahara Africa _Source: Wilson et al., 2004._ 30
Policy Research Working Paper 6987

# **Abstract**

Deeper regional integration can be beneficial especially for regions along international borders. It can open up new markets on opposite sides of borders and give consumers wider access to cheaper goods. This paper uses data from five contiguous districts of India, Nepal, and Bangladesh in the northeast of the subcontinent to measure the degrees of trade complementarity between districts. The paper illustrates that the regions are underexploiting the potential of intraregional commerce. Price wedges of up to 90 percent in some important consumption products along with measures of complementarity between households’ production and consumption suggest the potential for relatively large gains from deeper trade integration. Furthermore, an examination of a specific supply chain of tea highlights factors that help industries scale up, aided by institutions such as an organized auction and decent physical and legal infrastructure. However, districts alike in geography but located across international boundaries face different development prospects, suggesting that gains from reduced “thickness of borders” would not accrue automatically. Much rests on developing intrinsic industry competitiveness at home, including the reform of regulatory and business practices and infrastructural bottlenecks that prevent agglomeration of local economies.

This paper is a product of the Trade and Competitiveness Global Practice 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 mcali@worldbank.org, tfarole@worldbank.org and ckunaka@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
**Figure 26. Market Concentration (HHI Index) by Region**

<!-- Start of picture text --> North Caucasus<br>Far East<br>Siberia<br>Ural<br>Volga<br>South<br>Central<br>Northwest<br>0.00 0.20 0.40 0.60 0.80 1.00<br><!-- End of picture text --> Source: RUSLANA.

50
# **Table 1 - Causes of Corruption: Full Specification**

||WLS<br>Full Specification|Effect of One Standard<br>Deviation Change on<br>Corruption<br>(% Change Around the<br>Mean)|Rank of<br>Importance|Rank of Importance<br>(among significant<br>variables)|
|---|---|---|---|---|
|Government size|-0.1213<br>(-0.37)|-1.15|12|-|
|Complexity of Tax<br>System|0.2900<br>(1.51)|5.71|6|-|
|Degree of closed<br>economy|0.3206***<br>(3.89)|10.37|3|3|
|Lack of competitive<br>market structure|0.1320**<br>(2.63)|7.15|5|-|
|Laxity of bureaucratic<br>controls|-0.1138<br>(-1.01)|-2.00|11|5|
|Judicial fairness|-0.0355<br>(-0.26)|-1.09|13|-|
|Democratic Institutions|-0.2571***<br>(-4.27)|-15.54|2|2|
|Decentralization|-0.1722*<br>(-2.02)|-4.20|8|6|
|Lack of service<br>orientation in<br>bureaucratic culture|0.3324***<br>(3.54)|17.27|1|1|
|Colonial Past|8.8867**<br>(2.58)|8.23|4|4|
|Ethnic Heterogeneity|0.0387<br>(0.79)|2.46|10|-|
|Government Pay|-0.8768<br>(-1.29)|-4.35|7|-|
|Social Development|0.0712<br>(0.36)|2.50|9|-|
|Sample Size|30||||
|Adj. R sq.|0.9870||||

The dependent variable is the corruption index of TI where a higher value of the index corresponds to more corruption. The inverse of standard deviation of corruption index is used in the weighted LS regression. Errors are White-Heterocedasticity consistent t-statistics are in parenthesis – standard errors are White-corrected for heterogeneity. *** significant at p<0.01 ** significant at p<0.05 * significant at p<0.10 19
<!-- Start of picture text --> Congo<br>0.9<br>08<br>0.7<br>g os0.6<br>32 0.40.<br>£ 0.3<br>0.2<br>0.1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Burkina Faso<br>0.8<br>07<br>0.6<br>8 0.5<br>Bosso<br>£0.3<br>0.2<br>0.1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Nigeria<br>0.7<br>0.6<br>0.5<br>g<br>= 0.4<br>3<br>=<br>0.2<br>0.1<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Tanzania<br>1.6<br>1.4<br>12<br>gt<br>e<br>Jos<br>zoe<br>0.4<br>0.2<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Zambia<br>3<br>2.5<br>2<br>vo<br>eo<br>i=<br>B15<br>3<br>£<br>1<br>0.5<br>0<br>1990 1995 2000 2005 2010<br><!-- End of picture text -->
Table 9: Innovation intensity, 2016-2020.

|||All sample||Comp|etitive sect|ors|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|BOS|0.0305***<br>(0.00145)|||0.0273***<br>(0.00174)|||
|BOS * 2020|0.0138***<br>(0.00428)|||0.0196***<br>(0.00603)|||
|Public||0.0148***|||0.0107***||
|||(0.00183)|||(0.00180)||
|Mixed||0.0282***|||0.0248***||
|||(0.00228)|||(0.00279)||
|Participated||0.0579***<br>(0.00359)|||0.0733***<br>(0.00594)||
|Public * 2020||0.0192***|||0.0229**||
|||(0.00705)|||(0.00916)||
|Mixed * 2020||0.0112*|||0.0137*||
|||(0.00626)|||(0.00815)||
|Participated * 2020||-0.00236<br>(0.00807)|||-0.00650<br>(0.0136)||
|Ownership 10-_<_25%|||0.0488***<br>(0.00574)|||0.0747***<br>(0.0110)|
|Ownership 25-_<_50%|||0.0791***<br>(0.00709)|||0.124***<br>(0.0146)|
|Ownership 50-_<_100%|||0.0308***<br>(0.00214)|||0.0262***<br>(0.00258)|
|Ownership 100%|||0.0147***<br>(0.00181)|||0.0105***<br>(0.00181)|
|Ownership 10-_<_25% * 2020|||0.00285|||-0.00336|
||||(0.0133)|||(0.0246)|
|Ownership 25-_<_50% * 2020|||-0.00383|||-0.00313|
||||(0.0161)|||(0.0346)|
|Ownership 50-_<_100% * 2020|||0.00939*<br>(0.00553)|||0.0102<br>(0.00704)|
|Ownership 100% * 2020|||0.0170**<br>(0.00688)|||0.0201**<br>(0.00895)|
|Firm size|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm age|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector2d|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>Year|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|10762276|10762276|10762052|9827417|9827417|9827251|
|R-squared|0.152|0.152|0.152|0.162|0.162|0.162|

_Note:_ The table presents the results from firm-level regressions of innovation intensity on ownership status. All specifications include fixed effects to control for initial firm size, initial firm age, 2-digit industry and year. Innovation is measured as the share of workers in technical and scientific occupations (PoTec) relative to total employment for firm _f_ in year _t_ . Data from RAIS, from 2016 to 2020. Robust standard errors in parentheses. Significance levels: * 10%, ** 5%, *** 1%.16
# **Going Digital: Credit Effects of Land Registry Computerization in India**

Klaus Deininger and Aparajita Goyal<sup></sup> JEL: _G28, Q24, R51, R52_ Keywords: _Land Registration, Credit, Transactions, Computerization, India_

>  This paper would not have been possible without the support of the Reserve Bank of India, in particular Deputy Governor Rakesh Mohan and the Andhra Pradesh State Government’s Department of Stamps and Duties, in particular the Registrar, D. Reddy  and the Commissioner Survey and Settlement, V. Agrawal. We thank Markus Goldstein, and Vijayendra Rao for many useful comments. Funding from the Global Land Tools Network is gratefully acknowledged. The views expressed in this paper are those of the authors and do not necessarily reflect those of the World Bank, its Board of Directors, or the countries they represent. Address: Development Research Group, World Bank, 1818 H Street, NW, Washington, DC 20433, <u>kdeininger@worldbank.org, agoyal3@worldbank.org</u>