rafmacalaba/datause-encoder-data huggingface dataset:
in distortions across countries. The change in distortions realized by the median country over any two decades is small and remains in the range of ±0.2 in absolute terms. Contrarily, countries that are located at extremes report changes that are orders of magnitude larger than the modest change seen at the median.

# **4.2 Average and Marginal Gaps**

At this moment, it is worthwhile to compare the estimates of distortions that take into account the convergence of marginal productivity gaps to estimates that are obtained while using convergence in gaps in average productivity as a measure of efficiency. Figure 8 shows the estimated distortions using the two approaches for the 2000s.13 The horizontal and the vertical axes report the distortions estimated using the marginal gap and the average gap approach respectively. If the estimates from the two approach are similar, they lie on or close to the solid 45-degree line. The points below this line refer to cases where the average gap approach underestimates the distortions and the points above the line refer to cases where the average gap approach overestimates the distortions. The two gray lines helps in identifying how large the deviations are in estimates from the two approaches.

In general, the distortions estimated using average gaps in productivity are around 35% and 22% lower than when estimated using marginal gaps for Industry and Services respectively. This divergence in estimates is widespread. For the 88 countries in the sample, the distortions estimated using the average gaps are lower in 41 (60) countries in the case of Industry (Services). What is more important here is that the magnitude of difference in distortions estimated from the two approaches are quite large for many countries. For example in a quarter of the sample, the average gap method overestimates the distortions in Industry by around 50% and underestimates the distortions in Services in excess of 40%. Such large deviations imply that there are bound to be large deviations between quantitative exercises that employ the different approaches. Hence, considering the marginal gaps in productivity across sectors as a measure of efficiency is not only important for theoretical reasons, but also important for quantitative analysis as there are significant variation in labor shares across sectors.

I now begin with the two main quantitative exercises. The first quantitative exercise isolates the contribution of changes in distortions over time to growth realized during the same period. In the second quantitative exercise I explore the relationship between GDP per capita and counterfactual growth resulting from reducing current distortions to a common benchmark for all countries. An

> 13I present the distortions for the 2000s as an illustration, as the findings in this period would extend to other periods given that I assume invariance of factor shares over time. The choice of the 2000s is due to the fact that the distortions for maximum number of countries (88) are available for in this decade.

14
Table 1: Dependent variable: Backward GVC <u>participation</u> shares <u>(EORA)</u>

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Regressions|LS - BE|LS - BE|LS - BE|LS - BE|LS - BE|LS - BE|
|Avg. tari¤ rate (%)|-0.006***|-0.006***|-0.004*|-0.006***|-0.005**|-0.006***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|FDI in‡ows (log)|0.023**|0.023**|0.019*|-0.009|0.017*|0.022**|
||(0.010)|(0.010)|(0.011)|(0.013)|(0.010)|(0.010)|
|Distance to GVC hubs|-0.104***|-0.103***|-0.111**|-0.119***|-0.116***|-0.103***|
|(log)|(0.036)|(0.035)|(0.048)|(0.044)|(0.038)|(0.036)|
|Political stability index|0.030*|0.029*|0.024|0.040**|0.034*|0.025|
||(0.016)|(0.016)|(0.019)|(0.019)|(0.017)|(0.017)|
|Domestic industrial capacity|-0.027***|-0.028***|-0.027***|0.000|-0.015*|-0.027***|
|(log)|(0.008)|(0.008)|(0.009)|(0.011)|(0.009)|(0.008)|
|Resources rents / GDP|-0.003**|-0.003***|-0.002**|-0.002|-0.003***|-0.003**|
|(%)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Capital / GDP (log)|0.044*|0.046*|0.025|0.033|0.034|0.050*|
||(0.025)|(0.025)|(0.029)|(0.026)|(0.027)|(0.025)|
|Land / GDP (log)|-0.020***|-0.019***|-0.014**|-0.014|-0.019***|-0.020***|
||(0.006)|(0.006)|(0.007)|(0.009)|(0.007)|(0.006)|
|Med/High-skilled labor|0.012|0.012|0.005|-0.005|0.015|0.006|
|/ GDP (log)|(0.015)|(0.015)|(0.016)|(0.016)|(0.017)|(0.016)|
|Low-skilled labor|0.009|0.010|0.011|0.018|0.007|0.011|
|/ GDP (log)|(0.015)|(0.015)|(0.016)|(0.016)|(0.016)|(0.015)|
|Exch. rate|0.000||0.000|-0.000|0.214|0.000|
|appreciation|(0.000)||(0.000)|(0.000)|(0.403)|(0.000)|
|Misalignment||0.000|||||
|||(0.000)|||||
|NAFTA|||0.046||||
||||(0.068)||||
|EU|||0.079*||||
||||(0.044)||||
|MERCOSUR|||0.020||||
||||(0.071)||||
|ASEAN|||0.133**||||
||||(0.056)||||
|No. of trade partners|||-0.011||||
|(log)|||(0.038)||||
|Depth of Agreement|||0.009||||
|(log)|||(0.023)||||
|Time to import|||||0.000||
|(log)|||||(0.001)||
|Female participation||||||0.001|
|(log)||||||(0.001)|
|Observations|290|290|266|194|191|290|
|R-squared|0.528|0.530|0.546|0.464|0.505|0.532|
|Number of countries|121|121<br>|119|88|109|121|
|Decade ...xed e¤ects|YES|YES<br>33|YES|YES|YES|YES|

Notes: LS standard errors in parentheses. Column (4) estimates are based on a sample excluding high-income countries; ***, **, and * indicate signi...cance levels of 1%, 5%, and 10%, respectively.
TABLE 9

# DISTRIBUTION OF OUTSTANDING BANK CREDIT BY SOURCE (in percent, end of year data)

|YEAR<br>_________|free<br>credit<br>________________|government<br>directed<br>credit<br>____-<br><br>TOT|short-term<br>directed<br>credit<br>------------<br>__-________--_----_<br>AL CREDIT|investment<br>loans<br>----------|earmarked<br>loans|
|---|---|---|---|---|---|
|1971|38|62|18|14|30|
|1974|38|62|16|13|33|
|1979|29|71|22|7|42|
|1984|28|72|14|10|49|
|1986|35|65|10|9|47|
|||AGRICUL|TURAL CREDIT|||
|1971|17|83|36|33|14|
|1974|16|84|33|37|1S|
|1979|23|77'|28|13||
|1984|12|88|13|16|59|
|1986|23|77|9|12|56|

Source: Annual Bank Statistics, various issues.

answer this puzzle below when considering cost of debt.

Credit did not grow only in absolute terms, it also grew relative to product and capital (Table 7). In the economy at large, debt to product ratio increased from 0.30 in 1969 to 0.67 in 1986. Reported product in agriculture is not exactly comparable to product as calculated in GNP, but rates of growth are: debt to product ratio increased in agriculture between the 1969 and 1986 by a factor of 3.8, much more than the economy's average. Another measure of debt intensity is the ratio of credit to net capital. While for industry the ratio hovered around 0.7 since 1969, for agriculture it grew steadily from less than 0.20 in 1969 to nearly 0.80 in 1986.

The share of the moshavim in agricultural credit grew Dy 40 percent from 1971 to 1986 (Table 8). Multiply this by the growth of credit to agriculture (Table 6) and one gets that real credit to the moshavim grew 7 fold between 1970 and 1986. And this calculation is only for credit received by moshavim directly from commercial banks, it does not 27
**Table 1 Average predicted probabilities of employment in shadow economy according to all definitions of shadow employment and model specifications**

|Row|Model|Dependent variable|Years||||||
|---|---|---|---|---|---|---|---|---|
||||2003|2004|2005|2006|2007|2008|
|Basic v|ersion - b|asic definition of shadow em|ployment,a|ll variables|used in esti|mated mode|ls||
|1|Logit|Shadow employees|2.6%|2.9%|3.1%|3.2%|3.3%|3.2%|
|2|Probit|Shadow employees|2.6%|2.9%|3.1%|3.2%|3.3%|3.2%|
|3|Logit|Shadow workers|3.5%|3.7%|3.8%|3.9%|3.9%|3.7%|
|4|Probit|Shadow workers|3.5%|3.7%|3.8%|3.9%|3.9%|3.7%|
|Basic d|efinition o|f shadow employment – esti|mated mode|ls without ―|contract‖ v|ariable|||
|5|Logit|Shadow employees|2.7%|2.8%|2.8%|2.9%|3.1%|3.3%|
|6|Probit|Shadow employees|2.8%|2.8%|2.9%|3.0%|3.1%|3.3%|
|7|Logit|Shadow workers|3.6%|3.6%|3.6%|3.7%|3.7%|3.8%|
|8|Probit|Shadow workers|3.7%|3.7%|3.7%|3.8%|3.8%|3.8%|
|Narrow|definitio|n of shadow employment|(without t|hose with o|ral agreem|ents only)–|all variab|les used in|
|<br>estima|<br>ted models|<br>|||||||
|9|Logit|Shadow employees|0.5%|0.6%|0.6%|0.7%|0.7%|0.7%|
|10|Probit|Shadow employees|0.5%|0.6%|0.7%|0.7%|0.8%|0.7%|
|11|Logit|Shadow workers|1.6%|1.6%|1.6%|1.6%|1.6%|1.5%|
|12|Probit|Shadow workers|1.6%|1.6%|1.6%|1.7%|1.6%|1.5%|

Source: Authors calculations based on LFS data 2003-2008 and CASE 2006 survey database.

The resulting general trend estimates for 2003 -2004 from probit models for shadow workers (including employers) are presented on Chart 1 to better illustrate the results obtained. Only results of the probit models are presented for the sake of clarity, as the results did not differ substantially between logit and probit models‘ shadow employment proxies.

All estimations, where explanatory variables are used to obtain the proxy for shadow employment, show that the share of shadow employment was increasing between 2003 and 2006, and after 2006 it started to decrease. On the other hand, the results of estimations without the contract variable show something else – increasing share of shadow workers in total employment over the entire period. These two are the qualitatively different results but these differences seem to be relatively easy to explain.

**Chart 1 – Estimated general trends for shadow workers (probit models)**

<!-- Start of picture text --> 4.0% 1.7%<br>1.7%<br>3.9%<br>1.6%<br>3.8% 1.6%<br>3.7% 1.6% Shadow Workers -probit/ basic<br>1.6% Shadow Workers -probit/ basic/no<br>3.6% contract<br>1.6% Shadow Workers -probit/ narrow (right<br>axis)<br>3.5% 1.5%<br>1.5%<br>3.4%<br>1.5%<br>3.3% 1.5%<br>2003 2004 2005 2006 2007 2008<br><!-- End of picture text -->
**Table 5 Logit Regressions for the Incidence of Civil Wars (PRIOCW) in the Presence of -** **<u>Regional Dummies (1965 2005)</u>**

||(1)<sup>1</sup>|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|LGDPPC|-0.45*|-0.53*|-0.45|-0.52*|-0.65**|-0.66**|-0.65**|-0.56**|
|LPOP|0.37**|0.44***|0.44***|0.47***|0.53***|0.51***|0.50***|0.37**|
|PRIMEXP|0.11|-1.47|-1.74|-1.46|-1.63|-1.64|-1.64|-1.05|
|MOUNTAINS|0.01|0.01|0.01|0.01|0.01*|0.01|0.01|0.01|
|NONCONT|0.41|0.87|0.90|0.77|0.77|0.68|0.65|0.74|
|DEMOCRACY|0.10|0.60|0.60|0.71|0.69|0.68|0.65|0.32|
|ETHFRAC|0.22|0.38|0.30|-0.04|-0.07|0.01|0.03|0.19|
|ETHPOL|**2.05***|**1.78**|**2.00***|**2.36**|**2.56****|**2.69**|**2.64**|**2.21****|
|RELFRAC|-4.73|-3.93|-3.77|-3.85|-2.49|-2.70|-2.77||
|RELPOL|3.47|3.09|2.93|2.25|0.37|0.46|0.57||
|Int_nh|||1.08***|-0.32|-0.07|**-0.14**|**-0.31**||
|MENA|**Excluded**|**2.49****|**2.27****|**2.37****|0.98|1.71|1.74||
|SAFRICA|Included|0.73|0.83|0.89|0.84|0.60|0.63||
|LAAM|Included|0.17|0.20|0.24|-0.06|-1.89|-1.89||
|ASIAE|Included|1.27*|1.24|1.54******|1.85|2.46|2.46||
|Int_nh˟ RELPOL||||**2.95*****|**2.58****|**2.58****|**2.74*****|**1.47*****|
|Int_nh˟ ETHPOL||||-0.76|-0.92|-0.80|-1.00||
|RELPOL˟MENA|||||3.91|3.92|3.06||
|RELPOLxSAFRICA|||||0.70|0.80|0.72||
|RELPOL˟LAAM|||||1.15|0.44|0.42||
|RELPOL˟ASIAE|||||-0.40|-0.16|-0.36||
|ETHPOL˟MENA||||||-1.28|-1.21||
|ETHPOL˟SAFRICA||||||0.08|0.12||
|ETHPOL˟LAAM||||||2.84|2.85||
|ETHPOL˟ASIAE||||||-1.32|-1.232||
|Int_nh˟RELPOL˟MENA|||||||**2.05****|**4.33*****|
|Intercept|-5.81*|-7.61**|-8.39**|-8.12**|-7.98**|-7.46**|-7.42**|-5.35**|
|N|946|946|946|946|946|946|946|946|
|McFadden’s R<sup>2</sup>|0.154|0.198|0.224|0.238|0.250|0.254|0.257|0.210|
|McFadden’s Adjusted R<sup>2</sup>|0.120|0.162|0.184|0.195|0.198|0.192|0.193|0.185|

1 Column (1) shows results for the specification of Montalvo and Reynal-Querol (2005), shown in column (2) of Table 5 in their paper, with the data set extended to 2005.

28
If consumption data are available from both the base and target surveys, we can use an OaxacaBlinder type decomposition to formally test for Assumption 2 to shed further light on model selection. In particular, the change in poverty between the survey rounds can be broken down into two components, one due to the changes in the estimated coefficients (the first term in square brackets in equation (3) below) and the other the changes in the _x_ characteristics (the second term in square brackets in equation (3) below). Assumption 2 would be satisfied if the poverty change is mostly explained by the latter component. This can be expressed as Furthermore, if we make a stricter assumption about the error term in equation (1) following a standard normal distribution, that is ε _j_ | _x j_ ~ _N_ (,0 )1 , we can estimate equation (1) by a random effects probit model instead of the linear random effects model.

But the standard modelling tradeoff holds: if our stricter assumption is correct, estimation results are more accurate and vice versa. For comparison purposes, we will later present estimates using both the linear random effects and random effects probit models.<sup>13</sup> Following the estimation procedures in Dang et al. (2014b), our empirical implementation involves a two-stage process. First, we apply the estimated parameters from the 2004/05 round on the 2009/10 data to impute poverty for the latter. Since the questionnaires remain the same over these two survey rounds, their consumption data are comparable, and we can thus validate these estimated poverty rates against those based on the actual consumption data for the 2009/10 round.

> 13 We provide a Stata ado program named “ _povimp_ ” that automates the proposed estimation process (Dang and Nguyen, 2014). Type “ _ssc install povimp_ ” from within Stata (StataCorp., 2013) to download this program from the SSC Archive, which is maintained by Christopher F. Baum at Boston College. Our Stata program automatically allows for complex survey designs by offering an option to specify the variables indicating the clusters and the strata.

11
Table 4. Economic impacts (changes from the base case) <!-- Start of picture text --> ETS ETS-cum-RES<br>GDP (million yuan) -20152 (-0.073%) -28105 (-0.101%)<br>Welfare (million yuan) -10442 -13820<br>Investment (million yuan) 5037 (0.042%) 4221 (0.035%)<br>-Eastern regions (million yuan) -183 (-0.003%) -334 (-0.005%)<br>-Central regions (million yuan) 4301 (0.145%) 3833 (0.129%)<br>-Western regions (million yuan) 918 (0.038%) 723 (0.030%)<br>3%<br>ETS ETS-cum-RES<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>-3%<br>Figure 5a. GDP change in regions under ETS and ETS-cum-RES cases<br>15000<br>ETS ETS-cum-RES<br>10000<br>5000<br>0<br>-5000<br>-10000<br>-15000<br>GDP change<br>BJ TJ HB SX IM LN JL HLJ SH JS ZJ AH FJ JX SD HN HB HN GD GX HAN CQ SC GZ YN SAX GS QH NX XJ<br>Welfare change (Million Yuan)<br>BJ TJ HB SX IM LN JL HLJ SH JS ZJ AH FJ JX SD HN HB HN GD GX HAN CQ SC GZ YN SAX GS QH NX XJ<br><!-- End of picture text --> Figure 5b. Welfare change in regions under ETS and ETS-cum-RES cases In comparing the economic impacts between the ETS and ETS-cum-RES policy, we find as expected that the renewable energy mandate plus ETS policy would lead to 22
17 the main employment activity of all persons of working age from the ILO database. The results are very close to the LSMS-ISA based estimates (Table 1, column 1).<sup>24</sup> It further suggests that omission of the labor allocation to livestock and other agricultural subsectors beyond crop production does not affect the overall findings.<sup>25</sup> Tables 2 and 3 explore potential heterogeneity in labor allocation by gender across aggregate crop categories<sup>26</sup> and crop production activities. For instance, men are often thought to allocate disproportionately more of their time to (non-edible) cash crops, while women are believed to concentrate more on the production of staple and other food crops. Compared to the overall female share of agricultural labor, the female share allocated to non-edible crops is slightly lower in Malawi and Uganda, while it is slightly higher in Tanzania and a lot higher in southern Nigeria (Table 2).<sup>27</sup> Overall, while there is some variation in the female share of agricultural labor across crop categories within countries (especially Niger, Nigeria), these patterns are not generalizable across countries.

Turning to the crop production activities, land preparation is often considered a “male activity.” There are some signs of this, though only in Ethiopia and Niger (Table 3). There is hardly any variation in female labor allocation across agricultural activities in Tanzania, Malawi and Nigeria.<sup>28</sup> This is likely linked to the low degree of mechanized land preparation in SSA, with the exception of Ethiopia (and to some extent also in Niger), where the use of draught animals is more

> 24 The LSMS-ISA data for Ethiopia do not cover major urban centers, and are as such not comparable with the results obtained from ILO, which are national in coverage. The ILO database does not contain employment/occupation data disaggregated by gender for Niger.

> 25 The ILO estimates comprise all agricultural activities (crop and animal production, forestry and logging, fishery and hunting), as they are based on the International Standard Industrial Classification.

> 26 Computing female share of agricultural labor by aggregate crop categories, as opposed to individual crops, ensures sufficient number of observations per category on a cross-country basis, but could disguise intra-country variation across crops within an aggregate crop category.

> 27 The non-edible category includes tobacco, cotton, sunflower, sugar cane in Malawi, cotton in Niger, cotton, gum arabic, rizga, tobacco, jute, oil palm, palm oil, oil bean in Nigeria, cotton and tobacco in Tanzania, sugarcane, cotton, tobacco, coffee, cocoa, tea, ginger, curry, oil palm, and vanilla in Uganda.

> 28 For Uganda, the reported labor input was not disaggregated by activity domain.
principal-agent problems and improving risk management). Moreover, large banks can seize the benefits from scale in supplying non-lending products and services to a large number of firms, taking advantage of their service platforms, technical expertise, and IT and back-office infrastructures. Finally, large banks are better able than small banks to use sophisticated business models (e.g., business centers, branches, SME account managers, and marketing campaigns) and risk management systems, so as to combine and integrate centralized and de-centralized processes as appropriate to realize efficiency gains in managing both costs and risks. In sum, the ability to serve many SMEs (and for international banks, the ability to serve also many countries) through large multi-service platforms and branch networks and through sophisticated business models and risk management systems gives large banks a competitive edge, enabling them to compensate more easily for the fixed costs and switching costs of developing products and services to engage SMEs while exploiting economies of scale and scope.

The rest of the paper is organized as follows. Section 2 describes the data. Section 3 documents the degree to which SMEs constitute an important business for banks. Section 4 describes the business models that banks use to engage with SMEs. Section 5 presents evidence from the SME side. Section 6 concludes.

# **2. Data**

In this section we describe the data used to characterize the degree, determinants, and type of bank involvement with SMEs. In particular, we discuss the bank surveys performed in Argentina, Chile, Colombia, and Serbia by the World Bank, the cross11
Table A10: Impact on gender attitudes

||Gender<br>equality<br>index|Gender<br>equality<br>(conformity<br>to roles)|Ideal age<br>for a woman<br>to marry|Ideal age<br>for a man<br>to marry|Gap of<br>ideal age<br>to marry<br>women-men|Expected<br>education for<br>ado’s girls|Gap of<br>ideal<br>education<br>girls-boys|
|---|---|---|---|---|---|---|---|
|T1 (safe spaces)|0.269|0.260|-0.054|-0.982|0.697*|0.030|0.011|
||(0.238)|(0.246)|(0.487)|(0.609)|(0.407)|(0.085)|(0.073)|
|T1 + Leaders|0.006|-0.134|0.487|0.242|-0.168|0.029|0.057|
||(0.174)|(0.194)|(0.538)|(0.664)|(0.406)|(0.066)|(0.072)|
|T2 (safe spaces + IGA)|0.045|0.106|0.774**|0.574|-0.446|0.018|0.139**|
||(0.138)|(0.162)|(0.350)|(0.499)|(0.380)|(0.073)|(0.060)|
|T2 + Leaders|0.111|0.146|0.441|-0.169|0.500|-0.072|-0.003|
||(0.169)|(0.191)|(0.353)|(0.532)|(0.392)|(0.096)|(0.065)|
|T3 (safe spaces + husbands’ clubs)|0.313*|0.389**|0.365|-0.027|0.108|0.170**|-0.026|
||(0.174)|(0.170)|(0.354)|(0.546)|(0.486)|(0.071)|(0.051)|
|T3 + Leaders|0.179|0.163|1.353**|0.939|0.055|0.150*|-0.023|
||(0.194)|(0.176)|(0.525)|(0.640)|(0.319)|(0.077)|(0.052)|
|T4 (control spillover)|-0.042|0.063|0.390|0.057|0.067|0.021|0.048|
||(0.152)|(0.166)|(0.431)|(0.551)|(0.350)|(0.068)|(0.046)|
|T1 = T1 + L|.291|.135|.363|.0633|.0658|.991|.618|
|T2 = T2 + L|.689|.844|.279|.0855|.0287|.388|.054|
|T3 = T3 + L|.536|.236|.0474|.108|.911|.806|.956|
|T1 = T2|.329|.538|.0527|.00281|.0163|.895|.111|
|T2 = T3|.112|.112|.195|.176|.272|.0549|.00914|
|T1 = T3|.863|.618|.325|.0836|.279|.127|.613|
|T1 + L = T2 + L|.582|.208|.925|.484|.126|.294|.479|
|T2 + L = T3 + L|.745|.932|.0654|.0583|.251|.037|.76|
|T1 + L = T3 + L|.416|.155|.184|.329|.586|.119|.283|
|N|2,990|2,990|2,995|2,995|1,734|2,940|2,973|
|Control mean|1.03|1.22|18|22.9|-4.91|3.19|-1.04|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group. The p- values of the difference tests between the treatments are in the second part of the table. All regressions control for the baseline value of the outcome variable, stratification fixed effects, and the following baseline variables: adolescent girl’s age, education level, an indicator for being engaged in an IGA, knowledge of modern contraception, household is rural, household head is female, and at least one household member has an agricultural activity. When the baseline value of the outcome variable is missing, it is imputed and a dummy indicating whether the variable is imputed or not is added as control.

43
# **<mark>Table A2F – Parallel Trend Test of Firm Real Outcomes Across States of Varying Public Debt Before FD Law</mark>**

This table reports OLS estimates of the trends of real outcomes of private firms across states of varying public indebtedness prior to the introduction of the FD Law. The variable _Liabilitiesf,y_ is the volume (in logs) of liabilities of firm _f_ in year _y_ . _Assetsf,y_ and _Fixed Assetsf,y_ are the volume (in logs) of total and fixed assets of firm _f_ in year _y_ . _Salesf,y_ is the volume (in logs) of sales of firm _f_ in year _y_ . _DebtStates,16Q1_ is a measure of public indebtedness of state _s_ in the quarter prior to the FD Law and is calculated as the ratio of public debt of a state over its net income. _Trendy_ is a linear trend over time from the start of our sample in 2013 up to 2015, the year prior to the implementation of the FD Law. Observations at the state-year level. Standard errors are reported in parentheses and are doubled clustered at the state and year levels. *, **, *** denote significance at the 10, 5 and 1 percent levels. Detailed variable definitions are provided in Table A1 in the Appendix.

||Liabil|itiesf,y|Asse|tsf,y|Fixed As|setsf,y|Sal|esf,y|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Trendy|-0.07||-0.03||-0.19**||-0.05||
||(0.06)||(0.05)||(0.09)||(0.06)||
|Trendy*DebtStates,16Q1|0.01|0.01|0.06|0.06|0.06|0.06|-0.01|-0.01|
||(0.10)|(0.10)|(0.09)|(0.09)|(0.15)|(0.15)|(0.11)|(0.11)|
|Observations|654|654|654|654|652|652|654|654|
|R-squared|0.93|0.93|0.93|0.93|0.95|0.95|0.90|0.90|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|No|Yes|No|Yes|No|Yes|No|Yes|

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