rafmacalaba/datause-encoder-data huggingface dataset:
politically connected. While only 1.1 percent of firms in our data set were politically connected, they accounted for 15.8 percent of total output on the eve of the crisis. Last but not least, Suharto appointed mayors were allowed to finish their terms, which generates quasi-exogenous variation in the degree to which old-regime elites could capture local power (Martinez-Bravo et al., 2017,) and maintain anticompetitive privileges.

The main hypothesis tested in this paper is that disruption in political connections associated with Indonesia’s democratization had a pro-competitive impact on Indonesian manufacturing industries. If political turnover resulted in a reduction in anticompetitive practices that conferred advantages on companies connected with Suharto, one would expect companies connected to him to lose their privileges and market share after his removal from power. The impact of Suharto’s fall on firms with connections to him may be attenuated in districts where mayors appointed by him stayed in office longer. One would also anticipate greater competition and more vibrant firm dynamics in industries in which his family and cronies had extensive business interests, with greater improvements in industries where connected firms accounted for a higher market share.

A crucial identifying assumption is that the collapse of the Suharto regime reduced the value of connections to Suharto and attendant anticompetitive privileges received by politically connected firms. Fisman (2001) provides strong support for this assumption, showing significant movement in the stock prices of firms with connections to Suharto in response to news about his health. The online appendix B provides additional supportive evidence by showing that Suharto connections do not predict stock price movements to political events in the post-Suharto era.<sup>1</sup> Empirically, the main challenge is to isolate the impact of Suharto’s fall from potentially confounding changes caused by contemporaneous events such as the financial crisis, deregulation policies, and decentralization. Toward that end, we adopt two main strategies. To start with, we use difference-indifference and event study approaches in which we control for industry, year and district fixed effects as well as for Suharto era time-invariant firm and industry characteristics and allow their impact to differ across the years. We are not quantifying the impact of political turnover on firm performance and competition indicators per se, but rather examining whether the attendant disruption in political

> 1 The online appendix B demonstrates that the stock market valuation of firms with Suharto connections did not respond (differentially) to news events leading up to Wahid’s impeachment, for example, nor to elections. It also shows that industries in which the market share of politically connected firms expanded during the Suharto era witnessed a reduction in measures of competition, suggesting that political connections did repress industry-level competition during the Suharto era.

3
Figure 7a: Elasticities **of IMR.** 1986 with respect to Female Secondary Enrollment, 1976 & GDP, 1985 <!-- Start of picture text --> Elasticities of IMR<br>1 .<br>Ferm. Secondary Educ. GOP por capita<br>0.5 -<br>0~~~~~~~~~~~~~~~<br>-0.5 - - - - - - -- - -<br>.. . -. __§}| **-1-<br>0 10 20 90 40 50 00 70 80 90 100<br>900 600 1300 1000 2800 2800 3600 9800 4S00 4500 00(<br>L.#a;i. M earmilment. tmunltlon and GOP per capit<br>1101., Eleatalell. .m" ,...1.wa I lee *1. Tab le* 41. bdsia lI. we4eeea.<br>S4.ef.o e t b* t *1**1 yt:q".<br>IMese Vale. 300. WOOeL (1O0). 100. SOPar0 *.pim e1b08S .* 1.0. INA 100). so.<br><!-- End of picture text -->

<!-- Start of picture text --> Figure 7b: Elasticities of IMR, 1985 with respect to<br>Population per Physician, 1984<br><!-- End of picture text --> <!-- Start of picture text --> Elasticities of IMR<br>~~~~~~~~~~~~~~~~~~~~~~~1<br>Pop- per Phycian<br>0.5<br>1 4 t00<br>2O 000O9000oo003000i00007000 4000 1000<br>Levels of Population per Physician<br>l0t. I . . 4.b all other 14l0,1*0t<br>e.aaest as me.. Vale.<br>Wtoe*e. tpoelalma Par Pp"alelem 6114). tIsS. INN Irea6) so.<br><!-- End of picture text -->
<!-- Start of picture text --> (g) Total Reserves, Months of Imports, (h) Sovereign Spreads, Basis Points,<br>1990 to 2013 1995 to 2014<br>Argentina Average EM-G20 Argentina Average EM-G20<br>14.0 3000.0<br>Excluding 2002-<br>12.0 2500.0 2004 when ARG<br>10.0 2000.0 was in suspension<br>8.0 of payments<br>1500.0<br>6.0<br>1000.0<br>4.0<br>2.0 500.0<br>0.0 0.0 Russian crisis<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012<br><!-- End of picture text --> Sources: WDI; WEO Databases, Author’s calculations Table 1 provides information on period averages for Argentina and comparator countries for a variety of standard indicators. One sees that Argentina is slightly above average on the degree of general government and external indebtedness, but where Argentina really stands out is in terms of the volatility of growth (column 2) and sovereign risk (bottom of the last column). On the latter, the numbers for Argentina would be substantially higher if one were to include the debt default years in the period average. Similarly, if one compares Argentina to the comparator country average over time (Figure 1(a)-(h)), one sees persistently higher volatility and country risk – one indicator of economic policy credibility.<sup>3</sup> In brief, Argentina’s macroeconomic indicators look fairly “normal” for a large emerging market economy over the period, with the exception of modestly high indebtedness, and sharply higher volatility and country risk.

# **III. Macroeconomic policy**

# _The Historical Context and the Convertibility Period_<sup>4</sup>

Argentina emerged from extremely poor macro-economic performance in the 1980s to improved but highly volatile performance over the last twenty-five years. The 1980s was a period of debt distress, slow growth and periodic hyperinflation and adjustment. These conditions persisted into the difficult first two years of the Menem Administration who had been elected in 1989. Annual inflation ran into the thousands of percent for 1989 and 1990.<sup>5</sup> In March 1991, Argentina embarked on a new economic strategy with the Convertibility Plan. The centerpiece of the plan was the creation of a currency board arrangement, with the peso

> 3 In Figure 1(g), Argentina does not participate in the rising reserves of other Emerging Market G20 (EM-G20) countries.  It should be noted, however, that the rising reserve ratios of EM-G20 average is being driven by the sharp increase in reserves in several “BRIC” countries during the first decade of the century.

> 4 Annex 1 has a timeline of key political, economic policy and external events.

> 5 Buera et al, 2011, analyze the longer history of Argentine growth (200 years) and conclude that there was a secular slowdown in economic growth since 1974, accompanied by greater volatility in growth.  The latter is attributed to problems of fiscal management.

6
completely rule out the possibility that to demonstrate the effectiveness of its promotion activities, an IPA might response that its targeting decision was not driven by historical experience or in anticipation of future FDI inflow in a given sector while in reality it was indeed the case. In other words, the findings might potentially suffer from the selection bias resulting from unobserved characteristics of IPAs and associated false response bias in the survey.

# **4. Knowledge Gaps and the Way Forward**

The above discussion of the literature points out a number of critical knowledge gaps. **First,** in addition to having limited rigorous empirical evidence of the job impacts of different IC reforms, in most cases, we are unable to identify the gross vs. net job effects as well as shortterm vs. long-term job effects. **Second,** among other sources of bias, the estimated job effects of IC reforms from the cross-country enterprise survey analysis are also likely to suffer from survivorship bias, potentially leading to an overestimation of the job effects. **Third,** overall there is a relative scarcity of rigorous evaluation on the job effects of IC reforms in developing countries. Moreover, most of the existing rigorous evaluations are in the areas of business entry reforms, leaving many other important areas of IC reforms open for future research. Despite the stylized fact that majority of the informal firms are survival-entrepreneurs, the potential job effect can be substantial even if the thin slice of the high-potential entrepreneurs can graduate from the informal to the formal sector. Thus in the firm formalization space, a critical topic for future research should be how to identify these high-potential entrepreneurs and determine if and what type of IC reform(s) would induce job creation by moving the high-potential entrepreneurs from informal to the formal sector. Another critical area for the future research agenda is to identify if, how, and what IC reforms matter most for employment and productivity growth of the firms in developing countries over their life-cycle, as firms in developing countries tend to grow at a much slower pace than those in developed countries leading to a substantial crosscountry differences in aggregate total factor productivity (TFP). Even smaller differences in aggregate TFP over the long period of time can have dramatic consequences on per capita GDP of different countries. So this research agenda would be critical not only from job creation perspective, but also from the perspective of improving the overall level of development of a given country. Furthermore, unleashing the potential for firm growth in the formal sector could 31
# **Figure  1: Panel survey: sub-sample of  HIECS**

<!-- Start of picture text --> Jul-04<br>Aug-04<br>Sep-04<br>Oct-04<br>Nov-04<br>Dec-04<br>Jan-05 Panel<br>Feb-05 Feb-08<br>Mar-05 HIECS<br>2008/2009<br>Apr-05<br>Apr-08<br>May-05<br>May-08<br>Jun-05<br>Jun-08<br>HIECS Jul-08<br>Aug-08<br>2004/2005<br>Sep-08<br>48,000 HH Oct-08<br>Nov-08<br>Dec-08<br>Jan-09<br>Feb-09<br>Mar-09<br>Q2<br>Q3<br>Q4<br>Q1<br><!-- End of picture text --> The data used in this analysis rely on a one-month panel sample of the full HIECS 2004/05, containing the same (matched) households. Out of the 3,903 addresses from February 2005 sample revisited in 2008, 3,690 participated in the survey, of which 3,552 households were the same (panel) households as interviewed in 2005 and 138 were new households (at the old addresses). These 138 households do not form a panel and are not used for any analysis presented in this paper.

**_Main living standards indicator: consumption._** In this paper, as in the previous studies of poverty and living standards in Egypt (World Bank 2007), the analysis relies on actual consumption expenditure, including all money spending on consumer goods and services (durables included), and non-monetary parts, such as imputed rents, own production and in-kind transfers received by households. Food consumption includes food that the household has purchased, grown and received from other sources for 279 items. Non-food consumption is the sum of expenditure on 298 non-food items, including expenditure on fuel, clothing, schooling, health and several miscellaneous items.  Transfer and credit expenditure are also included. Compared to efforts deployed to capture each element of spending by a household, income module in the survey is rather light and relies on several aggregated items.

8
P-values are in brackets. *, **, *** represent statistical significance at the 10%, 5%, and 1% level respectively. Standards errors allow for clustering at the country level. All models are estimated via two methods: ordinary least squares (OLS) model and robust regression model. In dropping outlier OLS model, top 5% samples on each dependent variable are dropped except % of Women Borrowers. Though we do not present the coefficients to conserve space, all models include all of the following controls; annual % of inflation rate (GDP deflator), annual % of GDP growth, Rule of law , Bank overhead costs / Total assets, Ease of doing business index, Bank Credit / GDP, regional dummies (Europe and Central Asia, Latin America and Asia, Sub-Saharan Africa, South Asia and East Asia and Pacific, omitting Middle East and North Africa), lending methodology dummies (individual and solidarity lending, omitting village banking) and log of MFI's total asset values. Combined group dummy is 1 if one of the three status dummies (commercial bank, credit union / coop or rural bank) equals 1. NGO dummy is omitted in these models.

- - <u>Panel E: Formation/operation of non regulated microcredit institutions (0 4)</u>

|||<br>Version 1(Ful sampl<br>De|<br>es)<br>pvar: Average loan siz|Version 2<br>e /per capita G|<br> (DroppingRural b<br>NI|<br>ank samples)|
|---|---|---|---|---|---|---|
||OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|OLS|Robust Reg|OLS Dropping<br>top 5% of dep<br>var|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Formation/operation of non-regulated microcredit institutions (0-4)|-0.055|-0.0026|0.015|-0.0514|-0.0056|0.0097|
||[0.470]|[0.842]|[0.586]|[0.508]|[0.652]|[0.728]|
|NBFI dummy * Formation/operation of non-regulated microcredit institutions (0-4)|0.0369|0.0282|0.0311|0.0451|0.0291*|0.0387|
||[0.712]|[0.102]|[0.449]|[0.649]|[0.072]|[0.349]|
|Combined group dummy * Formation/operation of non-regulated microcredit institutions (0-4)|0.5282**|0.0517***|0.0257|0.5632**|0.0849***|0.0153|
||[0.037]|[0.007]|[0.739]|[0.025]|[0.000]|[0.839]|
|NBFI dummy|-0.0555|-0.0042|0.0503|-0.0628|-0.0116|0.0301|
||[0.803]|[0.915]|[0.634]|[0.777]|[0.756]|[0.776]|
|Combined group dummy|-0.8064|0.0318|0.1219|-0.8381*|-0.0772*|0.1498|
||[0.113]|[0.459]|[0.482]|[0.094]|[0.073]|[0.372]|
|Observations|641|641|616|583|583|560|
|Number of countries|39|39|39|39|39|39|
|R-squared|0.309|0.584|0.41|0.321|0.635|0.431|
|Adjusted R-squared|0.288|0.571|0.391|0.298|0.623|0.411|
|_Microscope index+ interaction term (P-value in brackets)_|||||||
|NBFI dummy|-0.0181|0.0256**|0.0461|-0.0063|0.0235**|0.0484|
||[0.849]|[0.034]|[0.217]|[0.947]|[0.038]|[0.191]|
|Combined group dummy|0.473**|0.049***|0.0407|0.512**|0.0793***|0.025|
||[0.030]|[0.000]|[0.575]|[0.018]|[0.000]|[0.726]|

29
is a dummy variable taking the value 1 when countries _i_ and _j_ did not share a colonial link, _NCLij_ is a dummy variable taking the value 1 when countries _i_ and _j_ do not share a common language, _NCLSij_ is a dummy variable taking the value 1 when countries _i_ and _j_ do not share a common legal system, and _NFTAij_ is a dummy variable taking the value 1 when countries _i_ and _j_ are not part of the same Free Trade Agreement.<sup>11</sup> We then substitute (2) into (1) and take logs on both sides to obtain:<sup>12</sup> where all _β_ s are parameters to be estimated and _βk_ = _εαk_ , where _k_ is the subscript indicating the different trade cost variables. Because we are interested in understanding the variation of different _β_ s offline and online, and because _Pi_ and Π _i_ are not observable (and difficult to estimate) we proceed as in much of the empirical literature and control for the multilateral resistance terms (and _yi_ and _yj_ ) including importer _i_ and exporter _j_ fixed effects.

A stochastic fixed-effect version of equation (3) is our baseline specification to understand the importance of different trade costs offline and online. We estimate it separately for online and offline flows, but also append the offline and online data so that we can test whether coefficients are statistically different online and offline by introducing an eBay dummy that we interact with each of the trade cost variables. If the interaction term is statistically significant then the offline and online coefficients are statistically different. In both cases we allow for importer and exporter fixed effects to be different online and offline. This captures differences in prices for online and offline products, and can also correct for a selection of buyers and sellers into online and offline platforms. We use a least-square dummy-variable estimator (LSDV), but also a Poisson estimator to control for heteroscedasticity (see Santos-Silva and

> 11Note that we measure the absence of common language, common legal system, colonial links or trade agreements, rather than their presence as in most of the literature. This has no consequences for the estimates, but it allows to interpret these variables as trade costs (like distance) rather than as trade-enhancing variables.

> 12Since some of our left-hand side variables were zeros (21 percent on eBay, less than 1 percent offline), we added a dollar the the import value before taking the logs.

9
Table 8: Sample plot shock causes households to substitute labor and input intensive irrigated horticulture away from most important plot

||Culti-<br>vated|Irri-<br>gated|Horti-<br>culture|Banana|HH<br>labor/<br>ha|Input<br>exp./ha|Hired<br>labor<br>exp./ha|
|---|---|---|---|---|---|---|---|
||(1)<br>|(2)<br>|(3)<br>|(4)|(5)|(6)|(7)|
|RDD (Site-by-seas|on FE, Sp|ecifcatio|n 3)|||||
|CA|0.076|-0.004|-0.004|0.096|-13.6|-3.3|0.2|
||(0.043)|(0.020)|(0.018)|(0.041)|(14.1)|(1.8)|(2.1)|
||[0.079]|[0.836]|[0.813]|[0.019]|[0.338]|[0.070]|[0.922]|
|CA * MIP CA|-0.089|-0.094|-0.080|-0.009|-44.1|-6.3|-4.7|
||(0.052)|(0.035)|(0.035)|(0.042)|(23.5)|(3.2)|(2.5)|
||[0.089]|[0.007]|[0.021]|[0.824]|[0.060]|[0.044]|[0.066]|
|Joint F-stat [p]|2.1|3.6|2.7|4.5|1.8|2.6|1.8|
||[0.122]|[0.028]|[0.070]|[0.013]|[0.164]|[0.078]|[0.175]|
|Sample plot efect<br>|0.005|0.162|0.137|-0.133|70.8|6.3|3.7|
|Average efect|0.038|-0.044|-0.038|0.092|-32.2|-6.0|-1.8|
|SFE (Spatial FE,|Specifcati|on 4)||||||
|CA|0.059|0.010|-0.007|0.087|-15.4|-3.8|1.5|
||(0.048)|(0.026)|(0.023)|(0.044)|(19.2)|(2.1)|(2.6)|
||[0.215]|[0.686]|[0.771]|[0.047]|[0.422]|[0.076]|[0.546]|
|CA * MIP CA|-0.121|-0.103|-0.066|-0.048|-39.7|-6.5|-4.5|
||(0.056)|(0.045)|(0.044)|(0.044)|(31.2)|(3.7)|(3.1)|
||[0.030]|[0.021]|[0.133]|[0.275]|[0.204]|[0.079]|[0.146]|
|Joint F-stat [p]|2.7|2.7|1.3|2.0|1.1|3.0|1.1|
||[0.070]|[0.069]|[0.286]|[0.139]|[0.324]|[0.050]|[0.345]|
|Sample plot efect<br>|0.022|0.171|0.156|-0.142|76.9|4.3|3.2|
|Average efect|0.004|-0.036|-0.037|0.065|-33.2|-6.7|-0.5|
|# of observations|2,179|2,179|2,179|2,179|2,166|2,169|2,169|
|# of clusters|182|182|182|182|182|182|182|
|Control mean|0.368|0.114|0.109|0.199|66.8|5.6|3.9|

_Notes:_ Regression coefficients on a command area indicator for the sample plot (“CA”) and its interaction with a command area indicator for the most important plot (“CA * MIP CA”) are presented above. Specifications control for distance to the command area boundary, its interaction with CA, log area of the sample plot and the most important plot, and a command area indicator for the most important plot (“MIP CA”). RDD specification includes site-by-season fixed effects, and SFE specification includes spatial fixed effects. Standard errors are in parentheses, and p-values in brackets. “Sample plot effect” estimates are from Table 4, and “Average effect” is the coefficient on CA from a specification that omits CA * MIP CA.

power by including most important plots outside the command area, which are almost never irrigated and have small allocations of labor and inputs36during the dry season. As discussed in the next paragraph, specifications which include the interaction of the sample plot command area indicator with a most important plot command area indicator are more precise for irrigation use, horticulture cultivation, and labor and input use.
# **Table 8**

Dependent Variable: Employment

|VARIABLES|(1)<br>OLS-FE|(2)<br>OLS-FE|(3)<br>IV-FE|(4)<br>IV-FE|(5)<br>IV-FE|(6)<br>IV-FE|(7)<br>IV-FE|(8)<br>IV-FE|(9)<br>IV-FE|(10)<br>IV-FE|
|---|---|---|---|---|---|---|---|---|---|---|
|POST|-3.05***|-1.06|132.93|225.82|-12.40|33.46|2.60|17.38|-3.73|20.36|
||(0.96)|(1.18)|(187.52)|(434.69)|(33.85)|(88.38)|(18.12)|(25.72)|(25.39)|(35.46)|
|POST_Dictator|-0.03|0.67|-70.48|-115.73|0.88|-18.77|-5.73|-11.04|-2.82|-12.68|
||(1.88)|(2.72)|(89.42)|(196.91)|(17.96)|(36.86)|(9.87)|(11.04)|(13.85)|(14.53)|
|POST_Labor|4.76***|2.56**|-66.50|-164.74|17.39|-26.89|6.47|-14.14|9.05|-14.39|
||(1.53)|(1.18)|(149.88)|(406.16)|(32.90)|(85.80)|(20.87)|(32.84)|(25.06)|(35.19)|
|Time|0.41***|0.18**|-4.64|-5.86|0.51|-0.29|0.13|0.09|0.43*|-0.12|
||(0.09)|(0.07)|(4.77)|(8.12)|(0.33)|(1.00)|(0.70)|(0.78)|(0.25)|(0.37)|
|Timesq|0.04***|0.05***|0.12|0.15|0.06**|0.08**|0.06**|0.07**|0.06**|0.07***|
||(0.01)|(0.02)|(0.07)|(0.12)|(0.02)|(0.03)|(0.03)|(0.03)|(0.02)|(0.03)|
|WorkingAgePop||0.00*||0.00||0.00||0.00||0.00|
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|rGDP||-0.00||-0.00||-0.00||-0.00||-0.00|
|||(0.00)||(0.00)||(0.00)||(0.00)||(0.00)|
|LFPRate||0.24||0.55||-0.09||-0.14||-0.13|
|||(0.23)||(2.26)||(0.68)||(0.60)||(0.61)|
|Observations|1,174|990|713|712|713|712|713|712|713|712|
|Number of Countries|79|71|59|59|59|59|59|59|59|59|
|R-squared|0.21|0.34|||||||||
|Partial R-squared|||0.00|0.00|0.01|0.00|0.01|0.00|0.01|0.00|
|Hansen J p-value|||.|.|0.04|0.04|.|.|0.71|0.84|
|Kleibergen-PaapF statistic|||0.17|0.08|0.21|0.07|0.91|0.38|0.52|0.40|

Note: Robust and clustered standard errors at the country level are in parentheses. *** p<0.01, ** p<0.05, * p<0.1. POST is instrumented with the debt-to-GDP ratio, terms of trade, and five-year change in democracy score in columns (3) and (4). In columns (5) and (6) the instruments also include lagged POST. POST is instrumented with external debt-to-GDP ratio interacted with the change in world interest rates, terms of trade, and five-year change in democracy score in columns (7) and (8). In columns (9) and (10) the instruments also include lagged POST.

34
# **Table 4 The Fee Income and Non-deposit Funding Shares**

The dependent variable in columns 1 and 2 is _Fee income_ , and the dependent variable in columns 3 and 4 is _Non-deposit funding_ . _Fee income_ is the share of non-interest income in total operating income. _Non-deposit funding_ is the share of non-deposit short-term funding in total deposits & short-term funding. _Assets_ is the natural logarithm of total assets in US $. _Equity_ is the capital to asset ratio, which is defined as equity as a percentage of total assets. We used one period lag of equity in the regressions. _Assets growth_ is the inflationadjusted growth rate of bank assets. _Overhead_ is defined as overheads as a percentage of total assets. _Investment bank dummy_ , _Non-bank credit institution dummy_ and _other dummy_ are dummy variables that equal 1 for investment bank, non-bank credit institution and other banks, respectively, and 0 otherwise. The data for these bank level variables are obtained from Bankscope. _Inflation_ is the inflation rate based on consumer prices. _GDP growth_ is the inflation adjusted growth rate of GDP per capita of the country. _GDP per capita_ is in thousands of 2000 constant US$. All these macroeconomic variables are from World Development Indicators (WDI) of World Bank. We estimate all regressions using country and time fixed effects and clustering at bank level. Standard errors are in parentheses. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Fee income|Fee income|Non-deposit funding|Non-deposit funding|
|Assets|0.034***|0.034***|0.029***|0.029***|
||(0.003)|(0.003)|(0.003)|(0.003)|
|Equity|0.183***|0.169***|0.009|0.014|
||(0.062)|(0.062)|(0.072)|(0.073)|
|Assets growth|0.036*|0.048**|0.046***|0.050***|
||(0.019)|(0.019)|(0.014)|(0.016)|
|Overhead|1.424***|1.461***|0.346**|0.349**|
||(0.213)|(0.218)|(0.147)|(0.150)|
|Investment bank dummy|0.291***|0.289***|0.184***|0.184***|
||(0.025)|(0.025)|(0.038)|(0.038)|
|Non-bank credit institution dummy|-0.022|-0.021|0.273***|0.273***|
||(0.043)|(0.043)|(0.052)|(0.052)|
|Other dummy|-0.039|-0.031|0.032|0.032|
||(0.029)|(0.029)|(0.021)|(0.022)|
|Inflation||0.783***||-0.191*|
|||(0.212)||(0.111)|
|GDP growth||0.318*||-0.158*|
|||(0.168)||(0.087)|
|GDP per capita||-0.003||0.005|
|||(0.004)||(0.004)|
|N|5511|5412|5472|5371|
|R-sq|0.566|0.573|0.398|0.397|
|Country fixed effects|Y|Y|Y|Y|
|Time fixed effects|Y|Y|Y|Y|
|Clusteringlevel|Bank|Bank|Bank|Bank|

38