rafmacalaba/datause-encoder-data huggingface dataset:
Hallerberg, M. and von Hagen, J. 1999. “Electoral institutions, cabinet negotiations, and budget deficits in the European Union.” In Poterba, J. and von Hagen, J. (eds.) _Fiscal Institutions and Fiscal Performance_ , University of Chicago Press: Chicago Jackson, R., A., (1996). “A reassessment of voter mobilization.” _Political Research Quarterly_ , Vol. 49(2): pp. 331-349 Kanazawa, S. (1998). "A possible solution to the paradox of voter turnout." _The Journal of Politics._ Vol. 60(4): p. 974-995 Landier, A., and D. Thesmar. 2005. “Optimistic beliefs as private benefits for entrepreneurs: Implications for contracting and evidence.” Mimeo.

Lindbeck, A., and J. Weibull. 1987. “Balanced budget redistribution as the outcome of political competition.” _Public Choice_ 52: 273-97.

Lizzeri, A., and Persico, N. (2001). “The provision of public goods under alternative electoral incentives.” _American Economic Review_ , Vol. 91(1): pp. 225-239.

Miguel, E. (2001). “ _Ethnic Diversity and School Funding in Kenya_ ”. Working Paper C01-119, Center for International and Development Economics Research, University of California, Berkeley.

Milesi-Ferreti, G.-M., Perotti, R., and Rostagno, M. (2002). “Electoral systems and the composition of public spending.” _Quarterly Journal of Economics_ , Vol. 117: pp. 609-657.

Mueller, D. and Stratmann, T. (2003). “The economic effects of democratic participation.” _Journal of Public Economics_ , 87: 2129-2155 Pacek, A., and Radcliff, B. (1995). “The political economy of competitive elections in the developing world.” _American Journal of Political Science_ , Vol. 39(3): pp. 745-759.

Perotti, R. and Y. Kontopoulos. 2002. “Fragmented fiscal policy.” _Journal of Public Economics_ 86: 191222.

Persson, T., Roalnd, G. and Tabellini, G. (2007). “Electoral rules and government spending in parliamentary democracies.” _Quarterly Journal of Political Science_ , Vol 2(2): 155-188 Persson, T. and G. Tabellini. (2004). “Constitutional Rules and Fiscal Policy Outcomes.” _American Economic Review_ , 94(1): 25-45 Persson, T., and Tabellini, G. (2003). _The Economic Effects of Constitutions_ . Munich Lectures in Economics, MIT Press, Cambridge: MA Persson, T., and G. Tabellini. (2000). _Political Economics: Explaining Economic Policy._ Cambridge, Mass.: MIT Press.

Persson, T. and Svensson, L. 1989. “Why a stubborn conservative would run a deficit: Policy with timeinconsistency preferences.” _Quarterly Journal of Economics_ , 104: 325-345.

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|**Table 8: Logit on wrong answers in the ES**|||||||||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1a)<br>yes_no|(1b)<br>no_yes|(2a)<br>yes_no|(2b)<br>no_yes|(3a)<br>yes_no|(3b)<br>no_yes|(4a)<br>yes_no|(4b)<br>no_yes|
|Firm is potential innovator Y:1 N:0|0.537***|0.429*|0.491**|0.624***|0.439|0.640|0.516|0.157|
||(0.173)|(0.226)|(0.194)|(0.225)|(0.316)|(0.518)|(0.331)|(0.545)|
|Manager's years of experience|0.002|0.008|||‐0.018|0.028|||
||(0.013)|(0.010)|||(0.020)|(0.022)|||
|Respondent experience with firm|||‐0.018|0.020|||‐0.003|0.055*|
||||(0.014)|(0.013)|||(0.030)|(0.030)|
|Respondent is woman Y:1 N:0|‐0.507*|‐0.168|‐0.665**|‐0.142|‐0.779*|‐0.560|‐0.896*|‐0.680|
||(0.274)|(0.345)|(0.306)|(0.368)|(0.455)|(0.526)|(0.481)|(0.611)|
|Use of show cards Y:1 N:0|||||0.188|0.128|0.110|‐0.142|
||||||(0.309)|(0.420)|(0.319)|(0.466)|
|Nr of interviews per supervisor|‐0.025***|0.045***|‐0.020*|0.043***|0.024|‐0.046|0.025|‐0.020|
||(0.008)|(0.014)|(0.010)|(0.014)|(0.032)|(0.049)|(0.032)|(0.048)|
|Enumerator's perception of truthful interv Y:1 N:0|0.054|‐0.379*|0.116|‐0.364|‐0.197|‐0.213|‐0.176|‐0.135|
||(0.154)|(0.198)|(0.172)|(0.230)|(0.317)|(0.417)|(0.342)|(0.511)|
|Length ES interview (minutes)|‐0.000|‐0.000|‐0.001|‐0.001|0.002|‐0.003|0.002|‐0.004|
||(0.002)|(0.002)|(0.002)|(0.003)|(0.004)|(0.005)|(0.004)|(0.005)|
|Average number of meetings with tax officials|0.004|0.010|0.001|0.023|0.003|‐0.013|0.011|0.074|
||(0.017)|(0.020)|(0.020)|(0.018)|(0.066)|(0.099)|(0.074)|(0.093)|
|Firm expected to pay informal payment Y:1 N:0|0.037|‐0.529**|0.107|‐0.379|‐0.312|‐0.814|‐0.433|‐0.711|
||(0.173)|(0.229)|(0.185)|(0.257)|(0.396)|(0.518)|(0.422)|(0.516)|
|Employment growth (%)|‐0.000|0.013**|‐0.001|0.015**|‐0.002|0.004|‐0.002|0.006|
||(0.005)|(0.006)|(0.005)|(0.006)|(0.009)|(0.009)|(0.010)|(0.010)|
|Size (log)|‐0.063|‐0.396***|‐0.082|‐0.533***|‐0.342**|‐0.651***|‐0.383**|‐0.574**|
||(0.067)|(0.081)|(0.075)|(0.091)|(0.167)|(0.238)|(0.183)|(0.247)|
|Age (log)|0.104|0.002|0.242|0.030|0.048|‐0.197|‐0.090|‐0.309|
||(0.138)|(0.162)|(0.178)|(0.167)|(0.263)|(0.302)|(0.339)|(0.355)|
|Foreign ownership (25+%)|0.149|‐0.854**|0.246|‐1.380***|0.745|‐0.882|0.784|‐1.666**|
||(0.346)|(0.420)|(0.351)|(0.443)|(0.517)|(0.870)|(0.519)|(0.848)|
|Export (directly or indirectly)|0.245|0.156|0.195|0.413|0.378|0.317|0.398|0.604|
||(0.198)|(0.291)|(0.226)|(0.301)|(0.371)|(0.592)|(0.383)|(0.615)|
|Constant|‐2.550***|‐3.582***|‐2.756***|‐3.557***|‐1.753**|‐2.714**|‐1.825**|‐2.773*|
||(0.497)|(0.815)|(0.537)|(0.884)|(0.818)|(1.262)|(0.855)|(1.421)|
|Observations|7,890|7,890|7,015|7,015|1,616|1,616|1,453|1,453|

Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 ; Country & ISIC 2‐digit FE not shown 33
**<mark>Table 2.  Drivers of cropland losses (pixel level)</mark>**

||Conversion out of<br>cropland|
|---|---|
|(Intercept)|.9886<sup>***</sup><br>(.1648)|
|Distance to city (in km)|.0001497<sup>***</sup><br>(.0000009628)|
|Number of drought months|.0005473<sup>***</sup><br>(.000002151)|
|Land suitability for agriculture|-.001499<sup>***</sup><br>(.000002584)|
|Distance to Coast (in km)<br>Distance to River (in km)|-.00001429<sup>***</sup><br>(.0000001413)<br>.00003348<sup>***</sup><br>(.0000007763)|
|Country fixed effect|Y|
|Observations|48,374,216|
|R<sup>2</sup>|0.06|

_Note: The regression is applied to all 2003 cropland pixels. The dependent variable takes value 1 if the pixel transitioned out of cropland over the 2003-2018 period and 0 otherwise. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1_ We then recover the country fixed effects from the above regression on the cropland sample and regress them on national level socio-economic and institutional variables as in specification (2). Given the small number of observations, we aim for a parsimonious specification, including cropland per capita—as a proxy for demographic pressure exerted on land utilization—an institutional variable, and controls for the macro-economic and socio-economic contexts. Table 3 below shows the coefficient estimates, starting with cropland per capita in column (1) and subsequently adding the Quality of Land Administration rate in column (2) as well as two controls in column (3): GDP per capita and the Gini Index. In columns (4) and (5), we substitute the Quality of Land Administration with other institutional variables, namely our index of Rural land tenure insecurity and the index for Enabling the business of agriculture, respectively. We do not include the institutional variables simultaneously due to both our concern to keep a parsimonious specification and the high correlation between these institutional variables. In Table 2, a negative sign is associated with a lower fixed effect and thus with a slower pace of cropland loss in regression (1).

For each variable, the sign of coefficients and the order of magnitude remain the same across specifications. Our preferred specification is column (3), which has the greater number of observations, although columns (3)-(5) consistently point towards significant comparable effects for both cropland per capita and the institutional variable. Column (3) shows that in countries that have greater cropland per capita, cropland disappears at a slower pace: A one standard deviation decrease in cropland per capita leads to 5.8 percent increase in cropland losses over the study period. This can be due to increased 11
(2001)). Rauch and Trindade (2002) show that the overseas Chinese network has promoted trade between countries with larger number of Chinese immigrants. Similarly, Combes, Lafourcade, and Mayer (2005) find that business and social networks play a similar role in promoting the exports of French firms. Chaney (2014) show how networks can be incorporated into theoretical models of international trade. Our paper provides evidence on how foreign banks create informational links between trade partners and help to increase trade The paper proceeds in three sections. Section 2 discusses the empirical methodology and the data used in the analysis. Section 3 presents the results on the relationship between foreign banks and exports in the Turkish sub-regions. Section 4 concludes.

# **2 Empirical Methodology and Data**

We examine the impact of foreign banks on Turkish international trade and in particular we aim to focus on the information channel through which they may operate. Foreign banks have strong ties with their parent country and are better placed to assess the profitability of a given product in that market and provide information about the export market conditions (information channel). With respect to this channel, we explore whether the extensive margin –the presence of the foreign bank– and the intensive margin –the footprint of the bank(s)– are economically and statistically significant. Foreign banks may also provide financing for firms that export to their home countries, which can blur the effects of the information channel. However, as discussed above, a large proportion of firms in Turkey do not use trade financing for their exports.

Exporters may gain easier and better access to finance when a larger number of financial services is present in the sub-region in which they operate (financing development). We control for the level of financial development or the amount of available financing in each sub-region, in order to net out the pure financing contribution to trade. Empirical evidence at both the aggregate and industry levels points to a positive relationship between sound financial markets and international trade. Using aggregate panel data of 30 years and 65 countries, Beck (2002) shows that countries with a higher level of financial development have (i) higher shares of manufactured exports in both GDP and total merchandise exports, and (ii) a higher trade balance in manufactured goods. Similarly, Manova (2008) shows that equity market liberalizations increase exports disproportionately in financially vulnerable sectors that require more outside finance or use fewer collateralizable assets. Addressing reverse causality, Manova (2013) shows that more thriving financial markets encourages growth in international trade, rather than the other way around.

4
27 3.4 and 3.8 percent is significantly lower than for any other group except for coal products (see Table 10). Hence, the largest concessions made by the EC pertained to the "least sensitive" markets in terms of protection offered to domestic producers.

The residual group accounts for a large sware of CEE-5 industrial exports to the EC. As can be seen from Table 10, in 1991 the country share of this group was between 32 percent (Romania) and 50 percent (Hungary). If exports below tariff ceilings and quotas which are duty free during the first year of the ITAs are included, then the free trade component-in terms of 1991 export baskets-goes up by 13 percentage points for Romania (to 47%), by 11 percentage points for the FCSK (to 55%) and Poland (to 54%), by 10 percentage points for Bulgaria (to 53%), and by 9 percentage points for Hungary (to 58%).

Table 10: The "Immediate Free Tradell Group: Exports to the EC nd Pre-ITAs Market Acess

||Expons<br>1991|Share in Indusrial<br>Expors. 1991|Index 1991<br>1988=100|NTB Coverage<br>Ratio,|Simple Average<br>Tariff Rate|<br>Range of<br>tariff.rates<br>Max-Min|
|---|---|---|---|---|---|---|
||(m USS millio|n)|(mp|ercnt)|||
|**Bulgaria**|293|43|132|3.6|5.6,|**.37.9-0**|
|FCSK|**2074**|**44**|188|3.8|5.7|39.7-0|
|Hungay|1680|50|218|3.7|0.0|14.0-0|
|Poland|2707|41|190|3;8<br>-|0.1<br>-|14J0-0|
|Romania|602|32|57|**_3A_**|0.0|6.2-0|

Sources: See Table 2.

Imports of the 'residual" products from _CEE-5_ counties increased more slowly than othr idusti groups. Between 1988 and 1991 the share of this group in CEE-5 idusrial imports declined except for imports from Hungary it increased by almost 7 prcentage points from.42 to 49 percentr(seef Table 11).41 It is noteworthy that better export performance, as evidenced by the declning share of a free trade "residual" group in CEE-5 industrial exports, in "more protected" than in "less protected" markets also indicates that access to sensitive markets did not significandy restrain impors in the 1988-91 period-except for steel "' A possible explanation is that Hungy's strategy of economic developmentundercetral planning had beenless biased in favor of heavy industry, i.e., steel and chemicals, ta other socialist economies. Since steel and chemical products (both accounang for a very sizble share-of CEE-5 wiports into the EC) in the EC as a ruleenjoy a higher level of protection than other products, Hungary's export basket-with a lower share of steel and chemical product-had a higher share of products less vulnerable to EC protectonist measures.
Dixit-Stiglitz price index for good _k_ in PR, defined as: Expenditures on final products _k_ are a fixed share ( _α_<sup>_k_</sup> ) of PR income, and therefore not endogenous to trade costs. Among final goods, the only behavioral response to _τj_<sup>_k_is substitution across sources of</sup><sup>_k_according to</sup><sup>_σk_.23</sup> Among upstream products, expenditure on product _k_ is driven by the destination region’s output mix, which is endogenous to trade costs. In the model, PR firms in sector _s_ purchase a bundle of inputs _A_<sup>_s_</sup> . The cost function for purchasing a unit of _A_<sup>_s_</sup> follows: where _w_ is the price of the productive factor(s), _μ_<sup>_s_</sup> _L_<sup>is the associated cost share,</sup><sup>_Ks_</sup> the set of upstream input products used in sector _s_ production, _P_<sup>�</sup><sup>_k_</sup> the conventional CES price index of _k_ , and _μ_<sup>_ks_</sup> the share of product _k_ in sector _s_ production. The _μ_ ’s are assumed fixed (within sectors and across locations), with _μ_<sup>_s_</sup> _L_<sup>+ �</sup> _k∈s_<sup>_μks_= 1.</sup> Production follows an increasing returns to scale technology. Sector _s_ uses the input bundle _A_<sup>_s_</sup> to produce _q_<sup>_s_</sup> according to:

> where _a_<sup>_s_</sup> 0<sup>and</sup><sup>_as_</sup> 1<sup>arethefixedandmarginalinputrequirements,respectively.The</sup> model differs from conventional theories that motivate the gravity model of trade because of scale economies and an associated entry condition.<sup>24</sup> Firms will not

> 23Our data are measured with origin (F.O.B.) prices. Following convention among estimators of _σ_<sup>_k_</sup> , (Fontagné et al. (2022) or Hummels (1999), for example), we consider the trade response of import value in estimation to be _−σ_<sup>_k_</sup> rather than 1 _− σ_<sup>_k_</sup> , as it would be for imports valued in destination-region prices. For this reason we include the quantity of iceberg melt on the left-hand side of (2), and ignore the effects of _τj_<sup>_k_on delivered quantities in estimation.</sup>

> 24The inequality that follows is taken from Balistreri and Rutherford (2013), who demonstrate mixed complementarity methods for computing monopolistic competition models of trade. Ques-

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26

- U.S. Congress, Joint Economic Committee, "Measures of Soviet Gross National Product in 1982 Prices: a Study," (Wash. D.C.: U.S. Government Printing Office, 1990).

- _ "USSR: Measures of Economic Growth and Development, 1950-80,"

- (Wash. D.C.: U.S. Government Printing Office, 1982).

- Wallis, John, Joseph and Douglass C., North, "Measuring the Transaction Sector in the American Economy, 1879-1970" in Long-term Factors in American Economic Growth, ed. by Stanley L. Engerman and Robert E. Gallman, NBER Studies in Income and Wealth, Vol.. 51 (Chicago: The University of Chicago Press, 1986) 95-164.

- World Bank, "Social Indicators of Development 1989" (Baltimore: John Hopkins, 1989).

- ___________ Socio-Economic Data Division, International Economics Department, "Accounting for CPE's in Transition: System-related Issues in Measuring Economic Performance," World Bank Research Proposal (1990).

- __________ World Develooment Report, (Washington: Oxford University for the World Bank, 1989, 1990, 1993).

- Zoteev, Genadii, "Sector of Services in an Economic System of Soviet Type" (Moscow: July 1990, monograph).
<!-- Start of picture text --> 61.8<br>61.8<br>61.75<br>61.6<br>61.7<br>61.65<br>61.4<br>61.6<br>61.55 61.2<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Normalized annual Normalized annual<br>accumulated consumption accumulated consumption<br>8.5 6.4<br>6.3<br>8<br>6.2<br>7.5<br>6.1<br>7<br>6<br>6.5 5.9<br>-20 -10 0 10 20 -20 -10 0 10 20<br>Normalized annual Normalized annual<br>accumulated consumption accumulated consumption<br>Days between final<br>measurements (0 to 1)<br>and bill issuance (period 0)<br>Days between final measurement<br><!-- End of picture text --> Figure 1 (cont.) Dates by Annual Accumulated Consumption _Notes:_ Dates are normalized so that May 1st, 2009 corresponds to day 1. Scatter points correspond to local averages computed by bins with a bandwidth of 1 unit of normalized AAC. Solid lines correspond to parametric fits generated from regressions that include a constant, a treatment dummy and a linear term for normalized AAC. Dashed lines are the 95% confidence interval.

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31

# **Notes to Flgures**

# A. Share of Services in Value Added: the USSR and OECD

|For OECD, value added shares are in current prices. For the USSR, 1970|
|---|
|<br>factor cost prices are used for the 1960s and 1970s, and 1982 factor cost prices|
|<br>are used for 1980s.|

|Figure 1:|_Share of Total Services (Including Military) in Value Added_<br>For OECD countries, military value added is included by definition, but in<br>practice some countries do not.<br>For the USSR, Figure 1 includes only the pay for military personnel.<br>Total services in the USSR also include a sector called "science and scientific<br>services," which other countries include partly in educational services and<br>partly in various production sectors.|
|---|---|
|Figure 2:|_Share of Total Services (Excluding Military) in Value Added_<br>Same as above, but without military personnel for the USSR.<br>Note: According to the CIA estimates, payments for military personnel - only<br>a part of value added of the defense sector - consist of about 2 percent of GNP<br>at factor cost. The OECD defense shares had been much smaller than those<br>of the USSR. Therefore, the two figures are shown here.|
|Figure 3:|_Share of Transport Services in Value Added_<br>For OECD, transport and storage are included; for the USSR, only transport.|
|Figure 4:|_Share of Communication Services in Value Added_|
|Figure 5:|_Share of Trade, Restaurant, and Hotel Services in Value Added_<br>For the USSR hotels are not included. They are in consumer services.|
|Figure 6:|_Share of Wholesale and Retail Trade in Value Added_<br>Data for the USSR include restaurants.|
|Figure 7:|_Share of Banking and Insurance Services in Value Added_|
|Figure 8:|_Share of Consumer Services (Including Housing for USSR) in Value Added_<br>Consumer services in all countries include repair, personal care, and<br>recreation. They do not include education and health services. Some OECD<br>data include housing, and some do not. It is usually not possible to separate<br>out housing. Therefore, consumer services data for OECD are the same in<br>Figure 8 and Figure 9.|
Figure 8: Property tax compliance: women and men respond similarly to personalized tax letters <!-- Start of picture text --> Timely Timely and late<br>payments payments<br>(a) Payment rates in levels<br>% paid the bill % paid the bill<br>Treated bill Treated bill<br>Oct'20 Oct'20<br>60 60<br>COVID-19 COVID-19<br>billing periods billing periods<br>50 50<br>40 40<br>Control (women) Control (women)<br>30 Control (men) 30 Control (men)<br>20 20<br>10 10<br>0 0<br>2019m1 2019m4 2019m7 2019m10 2020m1 2020m4 2020m7 2020m10 2021m1 2019m1 2019m4 2019m7 2019m10 2020m1 2020m4 2020m7 2020m10 2021m1<br>Billing Period Billing Period<br><!-- End of picture text --> (b) Treated vs Control <!-- Start of picture text --> Treatment Treatment<br>effect (p.p.) Treated bill effect (p.p.) Treated bill<br>7 Oct'20 7 Oct'20<br>6 WomenMen billing periodsCOVID-19 6 WomenMen billing periodsCOVID-19<br>5 4.7 p.p. 5<br>3.9 p.p.<br>4 4<br>3 3<br>2 2<br>1 1<br>0 0<br>-1 -1<br>-2 -2<br>-3 -3<br>2019m1 2019m4 2019m7 2019m10 2020m1 2020m4 2020m7 2020m10 2021m1 2019m1 2019m4 2019m7 2019m10 2020m1 2020m4 2020m7 2020m10 2021m1<br>Billing Period Billing Period<br><!-- End of picture text --> _Notes_ : These figures show the effect of the communication campaign on payment rates of pre- and post-intervention bills, for 24 consecutive monthly bills between January 2019 and December 2020. The left panels only consider timely payments, defined as bills paid before the 27th of the corresponding month (i.e., any payment made after the 27th is considered unpaid). Hence, pre-intervention bills mechanically exclude any past-due payment triggered by our intervention. The right panels consider timely and past-due payments made until December 2020 and, thus, capture backward payments triggered by our intervention (e.g., individuals who, after receiving the letter, pay the October 2020 and previous unpaid bills). The top figures show payment rates in levels for the control group (women in red and men in blue). The bottom figures report the treatment effects—i.e., the difference between treated and control units—and 95% confidence intervals for the 24 billing periods. These regressions control for tax compliance in 2018, zip codes, an indicator for undelivered letters, and an indicator for those paying no bills in 2018. The letters were delivered between September 28th and October 7th, 2020. The vertical dashed lines denote the start of the COVID-19 pandemic in Argentina and the month the letters were delivered. Each coefficient is estimated in separate regressions. Standard errors are clustered at the street-block level. We find that men and women respond similarly to the treatment letter. The intervention also nudged some people to catch up with unpaid bills from April 2020 onward, but the increased tax compliance is the same for men and women.

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