rafmacalaba/datause-encoder-data huggingface dataset:
database. Bank books, by contrast, are not always authored by Bank’s staff. Sometimes they are written by outsider experts commissioned specifically to write a volume the Bank feels could be valuable, but wouldn’t otherwise get written.<sup>20</sup> This prompts an obvious question: Might bibliometric comparisons of the Bank and other institutions be telling us more about the skills of Bank staff in identifying promising topics and consultants than about their skills as authors?

In this section we present bibliometric data on Bank _authors_ rather than Bank _publications_ . We compare Bank authors to authors elsewhere using data from the IDEAS project, based at the Department of Economics at the University of Connecticut using the RePEc database.<sup>21</sup> In these data, all an author’s publications catalogued by RePEc are allocated to her current institution irrespective of where she was when she wrote the piece and when publication occurred. This means, for example, that the Bank “captures” all a staff member’s publications from the years before she joined the Bank. It also means that when someone leaves the Bank, all her publications go with her to her new institution. These comparisons give a bibliometric view of the lifetime achievements of authors currently employed at the Bank compared to the lifetime achievements of individuals employed elsewhere.

Table 9 reports comparative bibliometric data by institution for authors registered with RePEc. The focus in RePEc is on economics publications. The database includes journal articles but also working papers and some book chapters. The figures in Table 9 report the institution’s ranking among the world’s top 5% of institutions (242 at the time of writing).<sup>22</sup> Among the 242 institutions that rank among RePEc’s top 5%, the Bank as a whole ranks first on journal pages, and first on number of works.<sup>23</sup> When the page count and the number of works are adjusted by the “quality” of the journal, the Bank slips to ninth and sixth among the 242 institutions.

What of citations when calculated on an author basis? The Bank comes third in terms of the total number of citations, but falls to 14<sup>th</sup> place when the citations are weighted by their “impact factor,” which is based on a ranking of the citing journals or volumes in terms of the citations they receive (Table 9). This reflects the fact that the Bank receives relatively more of

> African trypanosomiasis, Chagas’ disease, lymphatic filariasis and onchocerciasis, and malaria. TDR supports over one hundred TDR medical research institutes in IBRD and IDA countries.  See <u>http://apps.who.int/tdr/ for further</u> details.

> 20   Angus Deaton’s _The Analysis of Household Surveys_ is an example.

> 21   A paper by Coupé (2003) has been influential; however, his ranking of institutions was confined to economics departments in universities, although World Bank authors were included.

> 22   In Table 9 we include all Bank authors affiliated with RePEc irrespective of their department. 23  Adjusting by the number of authors makes no difference.

23
# DOES FOREIGN BANK PENETRATION REDUCE ACCESS TO CREDIT IN DEVELOPING COUNTRIES?

# EVIDENCE FROM ASKING BORROWERS *

George R. G. Clarke, Robert Cull, and Maria Soledad Martinez Peria _Development Research Group The World Bank_ Keywords: foreign bank penetration, small business lending JEL: G21,G32 Some of the data used in this paper are from the World Business Environment Survey (WBES) 02000 The World Bank Group. We would like to thank Gerard Caprio and Asli Demirgu,c-Kunt for helpful comments and advice. We are also grateful to Luke Haggarty and Andrew Stone for their help with the WBES data. _Contact information:_ Robert Cull, World Bank, 1818 H Street NW MC3-300, Washington, DC 20433. Phone: (202)473-6365. Fax: (202)522-1155. E-mail:rcull@worldbank.org.
sector financial markets, increased levels of democracy, and higher levels of trust’<sup>6</sup> Likewise, Dollar et al. (2005) state that a ‘range of empirical studies...find a relationship between long-run growth...and measures of institutional quality’<sup>7</sup> . The World Development Report 2005 underlines that the macro-analyses ‘generated useful insights – the most important is that secure property rights and good governance are central to economic growth.’<sup>8</sup> A recent paper by Durlauf, Kourtellos and Tan (2008) is more cautious. Investigating _‘_ the strength of empirical evidence for various growth theories when there is model uncertainty with respect to the correct growth model’<sup>_9_</sup> , the authors judge that ‘previous findings on the direct importance of institutions to growth are fragile’<sup>10</sup> . They do, however, conclude that there is at least ‘some evidence that institutions...play a role as determinants of growth rates’ even if ‘their effect is likely to flow through their influence on physical capital accumulation rates and not via TFP growth directly.’<sup>11</sup> Straub, Vellutini and Warlters (2008) find some evidence for a positive effect of infrastructure on growth, especially in poorer countries, but conclude that in general, the ‘results from studies using aggregate data lack robustness’<sup>12</sup> . Romp and de Haan (2005) highlight that recent estimations of infrastructure elasticities are much lower than earlier calculations that did not account for endogeneity effects appropriately.<sup>13</sup> Some econometric problems, such as the failure to account for model uncertainty in cross-section studies<sup>14</sup> , persist in the literature. Moreover, Romp and de Haan underline the considerable heterogeneity of the results across economies, arguing that the precise ‘channels through which infrastructure affects economic growth’<sup>15</sup> are still not understood very well. The consensus view that a broadly defined ‘business climate – institutions, infrastructure and the social environment – significantly affect economic performance is thus qualified by lingering concerns about the robustness and generality of specific results and the precise channels through which the estimated effects occur.

The macroeconomic approach is characterized by a number of _inherent_ limitations that suggest that microeconomic, firm-level analyses are required to achieve more robust results and more precise policy recommendations. These inherent limitations of the macro-literature include:

> 6 Pande, R. and C. Udry. 2005: 31.

> 7 Dollar, D., M. Hallward-Driemeier and T. Mengistae. 2005: 22.

> 8 World Bank. 2004: p.25;

> 9 Durlauf, S.N., A. Kourtellos and C.M. Tan. 2008: 329.

> 10 _Ibid._ :338.

> 11 _Ibid._ : 342.

> 12 Straub, S., C. Vellutini and M. Warlters. 2008: 23.

> 13 This is primarily because conceptual and econometric problems such as reverse causality or inefficient proxy variables have been at least partly addressed in more modern studies.

> 14 Romp, W. and J. de Haan. 2005: 57.

> 15 _Ibid._ : 58.

6
**<u>Table 9: Certification impact on size of land rented: marginal effects from tobit models</u>**

||**L**|**and rented-ou**<br>|**t**<br>||**Land rented-i**<br>|**n**<br>|
|---|---|---|---|---|---|---|
|||**Cham**|**erlain**||**Cham**|**berlain**|
|Land use certificates issued|0.073**|0.088***|0.092***|0.016|0.007|0.020|
||(2.30)|(2.77)|(2.83)|(0.65)|(0.29)|(0.78)|
|Total owned land in hectares|0.086***|0.086***|0.074***|-0.059***|-0.061***|-0.051***|
||(10.88)|(11.07)|(9.54)|(-6.24)|(-6.48)|(-5.45)|
|Share of good quality land|0.022|0.013|-0.002|0.003|0.008|0.006|
||(0.82)|(0.50)|(-0.09)|(0.10)|(0.31)|(0.25)|
|Number of dependents|-0.010|-0.002|-0.003|0.008|-0.013|-0.017|
||(-1.32)|(-0.10)|(-0.23)|(1.14)|(-0.96)|(-1.25)|
|Number of adult male|-0.013|-0.016|-0.027|-0.000|-0.041**|-0.039*|
||(-1.33)|(-0.70)|(-1.18)|(-0.03)|(-2.00)|(-1.88)|
|Number of adult female|0.016|0.008|0.016|-0.001|-0.004|-0.003|
||(1.53)|(0.41)|(0.83)|(-0.10)|(-0.20)|(-0.17)|
|Number of oxen|-0.152***|-0.094***|-0.095***|0.075***|0.041***|0.044***|
||(-13.29)|(-6.24)|(-6.30)|(8.56)|(3.29)|(3.34)|
|Value of other animals x 10<sup>-3</sup>(Birr)|-0.013***|-0.003|0.003|0.011***|0.009**|0.006|
||(-2.69)|(-0.50)|(0.52)|(3.71)|(2.10)|(1.37)|
|Roof corrugated iron sheet|-0.055**|-0.054|-0.061|0.069***|0.063*|0.073**|
||(-2.15)|(-1.38)|(-1.50)|(2.88)|(1.83)|(2.18)|
|Age of household head (years)|-0.006|-0.009**|-0.008*|-0.002|0.001|0.002|
||(-1.40)|(-2.29)|(-1.79)|(-0.43)|(0.24)|(0.47)|
|Age of household head squared|0.000***|0.000**|0.000**|-0.000|0.000|-0.000|
||(2.61)|(2.32)|(1.99)|(-0.67)|(0.34)|(-0.04)|
|Female headed household|0.191***|0.076|0.096*|-0.166***|-0.119**|-0.122**|
||(5.47)|(1.42)|(1.68)|(-6.41)|(-2.36)|(-2.47)|
|Household head can read and write|0.051**|0.063*|0.088**|-0.002|0.002|0.016|
||(2.10)|(1.69)|(2.25)|(-0.13)|(0.08)|(0.51)|
|Initial value of rented-out land in ha|||0.035<br>(1.57)||||
|Lag of rented-out land in hectares|||<br>0.082***<br>(3.48)||||
|Initial value of rented-in land in ha||||||0.112***<br>(6.34)|
|Lag of rented-in land in hectares||||||<br>0.137***<br>(8.37)|
|Year = 2007|0.052***|0.047**|0.039*|0.078***|0.082***|<br>0.077***|
||(2.61)|(2.34)|(1.81)|(4.23)|(4.13)|(3.50)|
|Number of observations|1424|1424|1302|1424|1424|1302|
|Number of households|736|736|736|736|736|736|
|Log lik.|-980.509|-960.667|-819.983|-1001.082|-987.542|-863.362|
|<br>Chi-squared|481.280|523.044|538.654|306.883|329.675|442.059|
|Rho|0.307|0.326|0.188|0.283|0.289|0.000|
|sigma_u|0.600|0.606|0.442|0.507|0.506|0.000|

**Note:** The Chamberlain specification includes the mean value of the time-varying household level variables (Chamberlain 1980), but not reported. A constant term is included in all the regressions. Absolute value of t statistics in parentheses; * significant at 10%; ** significant at 5%; *** significant at 1%.

26
included to control for monetary policy and exchange rate is included to better understand the transmission process of the trade shocks. It was not possible to add more variables due to data unavailability for many of the ASEAN countries.

GDP growth and trade data were collected from Oxford Economics and the IMF’s IFS dataset respectively. Since this paper investigates the spillover effects via the trade channel, it is important to define the trade variable correctly. Trade data for each country is the sum of the value of exports and imports of that country with other countries in the sample. Rather than using total trade data with all the countries of the world, using data specific to the countries in the sample helps to isolate the effects of trade within this region. Many analysts focus on the value of the trade balance (difference between exports and imports) of a country rather than using total trade (sum of exports and imports). However, the trade balance can be a poor indicator of the overall economic prosperity in the region. Trade, whether it is exports or imports, allows each nation to concentrate its labor, capital, and other resources on the economic pursuits at which it is most productive relative to other countries. This helps to generate greater output in the region which is shared by its participants, even though the effect on the trade balance might be negative in some countries. This is why total trade is used in this paper instead of the trade balance.

Real effective exchange rate data for China, Japan, Indonesia, Malaysia, the Philippines, Singapore and Thailand are taken from IMF’s IFS dataset. For other countries, exchange rate in terms of the US dollar is used as a proxy of the real effective exchange rate due to data unavailability. The source of short term interest data is OECD and Oxford Economics for China, Japan, Indonesia, Malaysia, the Philippines, Singapore and Thailand. For others the lending rate is used as a proxy for the short term interest rate and they are collected from the IMF’s IFS data set<sup>3</sup> .

In the individual VARX country models, all the four variables are included both as en3Except Brunei, Lao PDR and Vietnam, for which data were not available.

8
- Schalick, L. M., W. C. Hadden, E. Pamuk, V. Navarro, and G. Papas. 2000. “The widening gap in death rates among income groups in the United States from 1967-1986.” _International Journal of Health Services_ 30(1): 13-26.

- Sen, A. K. 1985. _Commodities and capabilities_ . Amsterdam, New York, N.Y.: NorthHolland.

- Sen, A. K. 1999. _Development as Freedom_ . Oxford: Oxford University Press.

- U.S. Bureau of the Census. 1963. “Methodology and Scores of Socioeconomic Status.” Working Paper No. 15. Washington, DC.

- van de Walle, D. 1995. “The distribution of subsidies through public health services in Indonesia, 1978-97.” In van de Walle and Nead, eds., _Public spending and the poor: Theory and evidence_ . Baltimore and London: Johns Hopkins University Press for the World Bank.

- van Doorslaer, E., A. Wagstaff, H. van der Burg, T. Christiansen, D. De Graeve, I. Duchesne, U. G. Gerdtham, M. Gerfin, J. Geurts, L. Gross, U. Hakkinen, J. John, J. Klavus, R. E. Leu, B. Nolan, O. O’Donnell, C. Propper, F. Puffer, M. Schellhorn, G. Sundberg, O. Winkelhake. 2000. “Equity in the delivery of health care in Europe and the US.” _Journal of Health Economics_ 19(5): 553-84.

- van Doorslaer, E., and A. Wagstaff. 1992. “Equity in the Delivery of Health Care: Some International Comparisons.” _Journal of Health Economics_ 11(4): 389-411.

- van Doorslaer, E., A. Wagstaff, and H. Bleichrod. 1997. “Income-related inequalities in health: Some international comparisons.” _Journal of Health Economics_ 16: 93-112.

- Wagstaff, A. 2000. “Socioeconomic inequalities in child mortality: comparisons across nine developing countries.” _Bulletin of the World Health Organization_ 78(1): 19-29.

- Wagstaff, A., P. Paci, and E. van Doorslaer. 1991. “On the measurement of inequalities in health.” _Social Science and Medicine_ 33: 545-57.

- Wagstaff, A., E. van Doorslaer, and P. Paci. 1989. “Equity in the finance and delivery of health care: Some tentative cross-country comparisons.” _Oxford Review of Economic Policy_ 5: 89-112.

- Wagstaff, A., E. van Doorslaer, and P. Paci. 1991. “On the measurement of horizontal inequity in the delivery of health care.” _Journal of Health Economics_ 10: 169-205.

- Wagstaff, A., and N. Watanabe. 2003. “What difference does the choice of SES make in health inequality measurement?” _Health Economics_ , in press.

- Wolfe, B. L., and J. R. Behrman. 1984. “Determinants of women’s health status and

- health-care utilization in a developing country.” _Review of Economics and Statistics_ 56: 696-703.

- Yitzhaki, S., and J. Slemrod. 1991. “Welfare dominance: an application to commodity taxation.” _American Economic Review_ 81: 480-96.

24
# **Figure 1: India’s growth rate has consistently accelerated over the long run**

<!-- Start of picture text --> 1 A: GDP growth has accelerated over the long 1 B: Per capita income growth has accelerated,<br>run too<br>13 y = 0.109*** x - 3.0 10 y = 0.13*** x + 0.50<br>11 t stat: 3.65, R2 = 0.25 8 t stat: 4.40, R2 = 0.324<br>9 6<br>4<br>7<br>2<br>5<br>0<br>3<br>-2<br>1<br>-4<br>-1<br>-6<br>-3<br>-8<br>-5<br>-10<br>-7<br>Real GDP Growth Real per capita GDP Growth<br>1 C: A clear trend of growth acceleration over 1 D: In terms of relative prosperity, the Indian<br>the long run is evident in 10-year averages, too economy has shown convergence<br>10-year rolling average: GDP growth 4.0<br>3.5<br>9<br>3.0<br>8<br>2.5<br>7<br>2.0<br>6<br>1.5<br>5<br>y = 0.1148*** x + 3.617 1.0<br>4 t stat: 21.7, R2 = 0.93 0.5<br>3 0.0<br>2<br>1<br>0 India's GDP as a share of World GDP<br>India's PCY as a share of US's PCY (constant 2010<br>US$)<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016<br>1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011 2014 2017<br><!-- End of picture text --> Note: Data are from Central Statistics Office, and World Development Indicators. In panel C, 10-year rolling averages of growth rate are for the current year and the preceding nine years. Years refer to respective fiscal years in Panels A-C and calendar years in panel D. *** p<0.01, ** p<0.05, * p<0.1.

**Figure 2: Growth rates have accelerated and become more stable across sectors**

2 A: A consistent acceleration in growth has 2 B: ...but is evident in industry not been observed in the agriculture sector...

6
As supplements to the Gini coefficient, we use the lowest and highest quartile of income shares and those are reported in Table 2 and Table 3. Data for these two quartiles are from Deininger and Squire (1998) and we again avetage over ten years. Other data are from the IMF's International Financial Statistics CD-Rom (April 1999 version) and World Bank database (2001). Detailed descriptions of definitions and sources of the data are given in the Data Appendix..

# (2). Empirical Findings

We used OLS (ordinary least squares), WLS (weighted least squares) to remove the possible heteroscedasticity in the cross-country data. Estimation results for OLS are reported in Table 5 and those for WLS are reported in Table 6.

As we can see from Table 5 and Table 6, the adjusted R-squared tends to be fairly high (around 40 to 50 percent) and estimation results are robust. Most of the control variables and our measure of inequality are significant and the significance and magnitude of the estimates do not vary much across specifications.

First of all, the log of Gini coefficient has the expected sign and is significant throughout all the specifications of the reduced form model. This provides support for the proposition raised in the theoretical part of the paper and it suggests that improvement in income distribution is associated with real exchange rate depreciation. It offers the imnportant policy implication that reducing inequality, by decreasing the price of non-tradables, will depreciate the real exchange rate. This depreciation will, by increasing the international competitiveness of tradable sector, improve the current account balance and accelerate the 13
||**Ap**<br>|**pendix Table**|**1: Data and Th**<br>|**eir Sources**<br>|||
|---|---|---|---|---|---|---|
|**Variable**|co2_emission_mil<br>_mettons_2008|pop_2008|gdp_ppp_percap20<br>08|mtco2e_1970t<br>o2006|g_bau|e_bau|
|**Description**|Carbon dioxide<br>emissions|Population in<br>year 2008|Gross domestic<br>product per capita|Cumulative<br>carbon dioxide<br>emissions from<br>1970 to 2006|Per capita GDP<br>growth in<br>business-as-<br>usual|Carbon<br>dioxide<br>emissions in<br>business-as-<br>usual|
|**Unit**|in millions metric<br>tons|in millions|in US dollars|in metric tons<br>carbon|in %|in %|
|**Source**|World Bank<br>(WB) World<br>Development<br>Indicators (WDI)|World Bank<br>(WB) World<br>Development<br>Indicators<br>(WDI)|International<br>Monetary Fund<br>(IMF) World<br>Economic Outlook<br>(WEO), April 2010|Climate<br>Analysis<br>Indicator Tool<br>(CAIT) World<br>Resources<br>Institute<br>(WRI)|Birdsall and<br>Subramanian<br>(2009)|Birdsall and<br>Subramanian<br>(2009)|
|**_Country_**|||||||
|China|6,534|0|5,999|89,775|4.1|1.1|
|US|5,833|0|47,393|185,369|1.7|0.8|
|Russia|1,729|0|15,941|69,225|3.1|0.0|
|India|1,495|0|2,790|22,768|5.2|2.8|
|Japan|1,214|0|33,957|38,639|2.1|‐0.6|
|Germany|829|0|35,539|35,713|2.3|0.1|
|Canada|574|0|39,080|16,640|1.9|0.5|
|UK|572|0|36,233|20,995|2.3|0.3|
|Korea_Rep|542|0|27,681|9,457|2.4|‐0.2|
|Iran|511|0|11,026|7,614|3.6|2.0|
|Saudi Arabia|466|0|23,489|6,316|2.7|1.9|
|Italy|455|0|30,520|14,651|2.2|‐0.3|
|South Africa|451|0|10,442|9,301|3.6|1.1|
|Mexico|445|0|14,528|10,214|3.3|2.1|
|Australia|437|0|38,356|9,486|1.9|0.6|
|Indonesia|434|0|3,980|5,812|4.8|2.8|
|Brazil|428|0|10,512|8,215|3.5|1.9|
|France|415|0|34,204|15,085|2.3|0.2|
|Spain|359|0|30,815|8,357|2.1|0.0|
|Ukraine|350|0|7,342|18,306|3.8|0.8|
|Poland|299|0|17,556|13,134|2.3|0.5|
|Turkey|272|0|13,107|4,897|3.8|2.0|
|Netherlands|264|0|40,558|5,966|2.3|0.0|

42
# **Figure 1 Commodities: price volatility and importance for exports and revenues**

**A. Commodity price movements since 2020**

**B** . **Share of EMDE exports of key commodities**

<!-- Start of picture text --> Percent Percent of exports<br>15<br>70<br>10 60<br>5 50<br>0 40<br>30<br>-5<br>20<br>-10<br>10<br>0<br>Oil Copper Coffee Natural<br>gas<br>C. Resource revenues as share of total fiscal D. Duration of commodity booms and slumps<br>revenues<br>Percent of fiscal revenues Months Booms Slumps<br>70 80<br>60<br>60<br>50<br>40 40<br>30<br>20<br>20<br>0<br>10<br>0<br>Energy Metal Agriculture<br>exporters exporters exporters<br>Oil Natural gas Coffee Rice Wheat Cotton Aluminum Copper Nickel Zinc<br>Energy Agriculture Metals<br><!-- End of picture text --> _Sources_ : UNU-Wider; UN Comtrade (database); WITS (database); World Bank. _Note_ : EMDEs = emerging market and developing economies; LICs = low-income countries.

A. Bars show the average month-on-month change in commodity prices from January 2020 to November 2023. Whiskers show the interquartile ranges. The commodities used (from the World Bank’s Pink Sheet database) are crude oil (average of West Texas Intermediate, Brent, and Dubai); natural gas index; coffee, Arabica; rice, Thai 5 percent; wheat, U.S. hard red winter; cotton, A Index; aluminum; copper; nickel; and zinc.

B. Panel displays the median export share of oil, copper, coffee, and natural gas for commodity-exporting EMDEs. The number of countries is 20 for oil, 6 for copper, 4 for coffee, and 5 for natural gas. Bars represent medians, while whiskers indicate interquartile ranges.

C. Unweighted average of resource revenues as a percentage of fiscal revenues for EMDE commodity exporters: 30 for energy, 14 for metals, and 10 for agricultural commodities. Whiskers show the interquartile range.

D. Duration measures the average length (in months) of a phase (boom or slump). Whiskers indicate minimum and maximum ranges.

27