rafmacalaba/datause-encoder-data huggingface dataset:
# **Antidumping and Safeguard Mechanisms: The Brazilian Experience, 1988-2003**<sup>**1**</sup>

Honorio Kume<sup>2</sup> Guida Piani<sup>3</sup>

# **1. Introduction**

In the late 1980s, the import-substitution industrialization process together with recurrent exchange rate crises led Brazil to implement an import policy whereby only those goods without a like product or those necessary to satisfy an unexpected spike in demand were allowed in. This policy consisted of high customs tariffs, discretionary controls (such as a list of forbidden items and an annual maximum limit for foreign purchases by enterprise), and special tax schemes whereby a substantial portion of imported goods was subject to tariff rebates or outright exemption.

From 1988 onwards, an import policy was adopted with the goal of fostering a more efficient allocation of resources through foreign competition. To minimize any potential political pressure, Brazil introduced in 1987, prior to the implementation of this new policy, a law that put into force agreements on antidumping, subsidies and countervailing duties, thus developing a new protection mechanism for domestic industries.

Therefore, jointly with a gradual import liberalization process, Brazil started to implement trade defense instruments for providing temporary relief to certain sectors when impacted by foreign competition. An efficient and judicious management of these mechanisms was essential not only to 1 Our acknowledgement to Leane Naidin and attendees at the Seminar "Use of Safeguards and Antidumping in Latin America" organized by J. Michael Finger and Julio J. Nogués, with the support of the World Bank, held between the 23 and 25 May, 2004, in Buenos Aires. We are especially grateful to Julio Berlinski, Julio J. Nogués and J. Michael Finger for his comments.

2 Instituto de Pesquisa Econômica Aplicada (IPEA) and Universidade do Estado do Rio de Janeiro (UERJ). E- mail: kume@ipea.gov.br

> 3 Instituto de Pesquisa Econômica Aplicada (IPEA). E-mail: guidapiani@ipea.gov.br

2
|**_Table 7cco_**|**_ntinued:_**<br>_50%_|**_repla_**|**_ceme_**<br>_ra_|**_nt_**<br>_te_||||||___|___|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|<br>-|m<br>j%dY=0%||||d|Y 1%||d|Y=1.5%||d|Y=2%||
|Contribution|age|18|21|25|18|21|25|18|21|25|18|21|25|
||IDeath|||||||||||||
|Real i = 4%|||l__|_____||||||||||
|Retire at 55|80|13|14|17|15|17|19|17|18|21|18|20|22|
||81|13|14|17|16|17|20|17|19|<br>21|19|20|23|
||83|14|15|18|16|18|20|18|19|22|19|21|24|
||85|14|15|18|17|18|21|19|20|23|20|22|24|
|Retire at 60|80|9|10|1l|11|12|13|12|13|15|13|14|16|
|___|_81|9|10|11|11|12|14|12|13|15|14|15|16|
||83|10|10|12|12|13|14|13|14|16|15|15|17|
||85|10|11|13|12|13|14|14|15|16|15|16|18|
|Retire at 65|80|6|6|7|8|8|9|9|9|10|10|10|I1|
||81|6|7|8|8|8|9|9|9|10|10|11|12|
||83|7|7|8|8|9|10|10|10|11|11|11|12|
||85|7|8|9|9|10|II|10|11|12|11|12|13|
|Retire at 70|80|4|4|_5_|5|5|6|6|6|7|6|7|7|
||81|4|4|5|5|6|6|6|6|7|7|7|8|
||83|4|5|5|6|6|7|7|7|8|_8_|_8_|8|
||_ 85|5|5|6|6|7|_8_|7|_8_|8|8|9|9|

37
mobility costs to US levels from Artuc and McLaren (2015). This simulation delivers a response of real wages that is represented by the dotted line in Figure 2. The overshooting, which is mild in the xed- _K_ model and the estimated mobility costs, become much sharper when _C_ is lower. Given the size of the shock, this higher labor mobility induces a suciently large outow of labor to actually compensate for the declining capital stock. In the new steady state, real wages would decline by 9.15 percent. Compared with the initial drop of 15.04 percent, this implies an overshooting of real wages of almost 6 percentage points.

These ndings have relevant implications for the interpretation of some of the results recently found in the literature. Our structural model with market frictions shows that tari reforms can actually have long-lasting impacts and that the patterns of adjustment, especially in the long-run, strongly depend on the nature of capital adjustment costs. In a setting with xed capital, while tari cuts positively correlate with wage changes in the short-run, this correlation can become negative in the long-run. In models with capital adjustment costs, tari changes positively correlate with wage changes not only in the short-run but possibly also in the long-run. The notion that capital adjustment costs can play a role is discussed in Dix-Carneiro (2014). Using ad-hoc imperfect capital adjustment rules, he shows that sluggish capital responses can aect the wage dynamics. Dix-Carneiro (2014) corroborates that, as expected, wages respond negatively to an adverse trade shock in the short-run but that, along the transition, these responses can become attenuated or amplied. There is also solid reduced-form evidence showing that the long-run wage responses can be larger than the short-run eects (Dix-Carneiro and Kovak, 2017). The results of our paper, based on a full structural estimation, put these intuitions into context and quantitatively elucidate how capital and labor market frictions interact in shaping them.

# 4.3 Trade Shocks and the Nature of Capital Adjustment

We model capital frictions in a complex way, which includes xed, convex, and irreversibility costs. We do this because the nature of the capital adjustment costs function plays an important role in the way the economy responds to trade shocks. To show why our complex structure of capital adjustment costs matters, we compare the evolution of the economy in two experiments. One is our benchmark case with full tari cuts in textiles, which is a negative shock to the sector. The other is the opposite case of a positive shock to the textile sector (for example due to tari protection). To get the comparison right, we set the price increase equal to the negative of the price decline in 23
We assume a flexible exchange rate with fixed foreign savings. This is a common specification in real trade models. It is assumed that any changes in the trade balance are determined by macroeconomic forces working mostly in asset markets which are not included in the model.

# **4. The Data**

The South Africa SAM is based on 2001 data including:

- SSA IO 1971-1993

- SSA SUT 1993-1998

- SAM 1998

- SARB published and unpublished data 1970-2001

- SSA industry censuses and surveys

- 1970-1996 population census

- OHS 1994-1999

- LFS 2000-2002

- HH Income and expenditure survey 2000

- McGregor BFA 1970-2001

- ASSA 2000 Demographic model

- RSA Standardized Industry Database developed by Quantec.<sup>11</sup>

The SAM consists of 49 commodities at industry level as well as 49 activities. The government produces six of the 49 activities. There are four factors of production, capital, high-skilled, semiskilled and unskilled labor. The households are divided into the 10 income deciles. Due to the magnitude of the 10<sup>th</sup> decile it is further divided into 95 percent, 96.25 percent, 97.5 percent and 98.75 percent.<sup>12</sup> Elasticities used in the model are from Gibson(2003), Van Heerden and Van der Merwe (1997), the CGE model of Lewis (2001) and Thurlow and Van Seventer (2002).

From the data in the SAM, we compute effective tax rates. These are the tax rates that change when we evaluate tax reforms. In South Africa, there are indirect taxes levied on either products or production and direct taxes levied on households and firms. Indirect taxes on products include value added tax, fuel levies, excise duties, and tariffs, while indirect on production would include payroll taxes.

# **5. Simulations and Results**

First, we evaluate the current tax structure. We remove the VAT and consider the following revenue neutral tax changes: (1) a uniform adjustment to all direct taxes on households and firms; (2) a uniform adjustment to income taxes for households in the 10<sup>th</sup> decile; and (3) a uniform adjustment to all commodity tax rates. The first scenario allows us to evaluate the

> 11 The data construction was commissioned by the World Bank in 2002/2003 and developed by Claude Van der Merwe from Quantec. See Van Der Merwe (2002) for a description of the sources used.

> 12 The SA SAM (2003) includes residuals on the capital factor payment column as well as residuals on the commodity receipts row.  The capital factor residual was removed by assuming the capital factor returns of firms contains undisclosed items, the factor returns was increased as well as the savings of firms to the same extent.  The residual in the commodity row was entered into the change in stock variable.  A balanced SAM was estimated using entropy by assuming the production technologies and the tax structure is known.

12
||(6.04)|(6.33)|(4.59)|(-3.17)|
|---|---|---|---|---|
|**MEXICO**|||||
|fli|0.20|0.82|0.36|_-0.59_|
||(0.99)|(8.91)|(1.79)|(-3.47)|
|1li3.<br>1|0.22|0.87|0.31|-0.59|
||(1.13)|(8.34)|(1.60)|(-3.5)|
|fli..2|0.22|0.69|0.3|-0.59|
||(1.07)|(6.81)|(1.42)|(3.39)|
|**TURKEY**|||||
|fli.|0 76|0.004|0.30|0.87|
||,*_-6I_|(0.01I9)|(1.50)|(8.97)|
|Ol.;|'!'4|0.003|0.28|0.89|
||S122,|(0.02)<br>|(1.42)<br>|(9.46)<br>|
|||0.04|0.24<br>|0.89|
||,; ?'7|jO.24)|(I.I9)|(9.39)|
||.o043|0.64|0.24|0.2|
||.-1.72)|(3.02)|(0.87)|(0.87)|
||!-063|0.65|0.12|0.16|
||(-,.83)|(3.09)|(0.44)|(0.67)|
||-0 38|0.64|0.3|0.02|
||(.1.40)|(2.79)|(1.09)|(0.22)|

**Notes:** I statistics in parcnthcsis. Private credit denotes the stock of credit to the private sector.

**41**
# **Table 2.  Change in Food Assistance Following the 2017 IPC Announcement**

||(1)<br>Share<br>Receiving<br>Food|(2)<br>Share<br>Receiving<br>Food|(3)<br>Share<br>Receiving<br>Food|(4)<br>Share<br>Receiving<br>Food|(5)<br>Share<br>Receiving<br>Food|
|---|---|---|---|---|---|
|VARIABLES|Assistance|Assistance|Assistance|Assistance|Assistance|
|Post Announcement Indicator|0.193***|0.177***|-|0.139***|0.138***|
||[0.023]|[0.025]||[0.021]|[0.021]|
|Quarter Indicators||||||
|____________________<br>Q2 2017 Indicator|-||-0.001|-|-|
||||[0.026]|||
|Q3 2017 Indicator|-||0.200***<br>[0.031]|-|-|
|Q42017 Indicator|-||0.220***|-|-|
||||[0.027]|||
|Q1 2018 Indicator|-||0.202***|-|-|
||||[0.026]|||
|Q2 2018 Indicator|-||0.252***<br>|-|-|
||||[0.036]|||
|Q3 2018 Indicator|-||0.227***|-|-|
||||[0.042]|||
|Interaction Between IPC Status and the Post<br>Announcement Indicator||||||
|____________________<br>Post Announcement Indicator*IPC 4 Indicator|-||-|0.095**<br>[0043]|-|
|Interactions Between the Post Announcement<br>Indicator and Individual Governorates in IPC 4<br>Regions||||.||
|____________________<br>Post Announcment Indicator*Abyan Indicator|-||-|-|0.064***<br>|
||||||[0.021]|
|Post Announcment Indicator*Al Hudaydah Indicator|-||-|-|0.008|
||||||[0.028]|
|Post Announcment Indicator*Hajjah Indicator|-||-|-|0.028<br>[0.021]|
|Post Announcment Indicator*Lahj Indicator|-||-|-|0.233***|
||||||[0.021]|
|Post Announcment Indicator*Sa'ada Indicator|-||-|-|0.279***|
||||||[0.021]|
|Post Announcment Indicator*Shabwah Indicator|-||-|-|0.016<br>[0.021]|
|Post Announcment Indicator*Taizz Indicator|-||-|-|0.130***|
||||||[0.021]|
|Observations|357|357|357|357|357|
|Controls and Governorate FixedEffects|N|Y|Y|Y|Y|
|Note: This table estimates how the share of responde<br>announcement of the 2017 IPC in March 2017. Colum<br>share of respondents in each governorate on an indica<br>column (2) adds governorate fixed effects and control<br>replaces the post indicator with indicators equaling on<br>column (4) interacts the post indicator with an indicat<br>announcement (IPC 4); and column (5) further interact<br>governorate that was classified as IPC 4 2017 IPC anno<br>killed in the month before the survey, and four lags of<br>governorate and reported in parentheses. * denotes si<br>5 percent level; and***denotes significance at the 1 p|nts receiving<br>n (1) estimate<br>tor equaling<br>variables; col<br>e if the surve<br>or for the mo<br>s the post in<br>uncement. C<br>the same var<br>gnificance at<br>ercent level.|food assistan<br>s a sparse sp<br>one if the su<br>umn (3) estim<br>y happened<br>st severe IPC<br>dicator with g<br>ontrol variab<br>iable. Stand<br>the 1 percen|ce changed f<br>ecification re<br>rvey was con<br>ates an iden<br>during that q<br>classification<br>overnorate i<br>les include th<br>ard errors are<br>t level; ** de|ollowing the<br>gressing the<br>ducted after<br>tical specific<br>uarter-year t<br>in the 2017<br>ndicators for<br>e number of<br>clustered b<br>notes signific|<br>average<br>March 2017;<br>ation but<br>ime period;<br>IPC<br>each<br>people<br>y<br>ance at the|

22
<!-- Start of picture text --> Bi Built-up in 1990 [) Built-up expansion between 1990 and 2015<br>Share of<br>Low expansion<br> income 2.8%<br>Share of<br>Lower-middle income expansion<br>23.4%<br>Share of<br>Upper-middle income expansion——<br>Share of<br>High Income expansionoa<br>a<br>J 50,000 100,000<br>Built-up (km2)<br><!-- End of picture text -->
wider international networks, which although an interesting possibility is beyond the scope of the current study.

# **-** **<u>Table 7: Results for Country level Immigrant Interactions</u>**

|**Country**|**Mig. Stock**|**Coeff. No**τij|**Sig. **|**Coeff. Inc.**τij|**Sig. **|**Country**|**Mig. Stock**|**Coeff. No**τij|**Sig. **|**Coeff. Inc.**τij|**Sig. **|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Australia-Norfolk**||||||**Hong Kong SAR,**||||||
|**Is.**|240,778|0.20|***|-0.02||**China**|287,117|0.11|***|-0.01||
|**Philippines**|1,237,433|0.19|***|0.05||**Oman**|75,655|0.11|*|-0.08||
|**Venezuela**|162,537|0.19|***|-0.01||**Slovenia**<br>**South Africa-**|13,231|0.11|**|||
|||||||**Botswana-Lesotho-**||||||
|**Angola**|172,856|0.18|**|-0.19|*|**Namibia-Swaziland**<br>**United Arab**|203,721|0.11|**|-0.03||
|**Chile**|342,971|0.18|***|-0.12||**Emirates**|17,467|0.09|*|-0.09||
|**Malaysia**|494,239|0.18|***|0.04||**Pakistan**|5,621,668|0.09||-0.14|*|
|**Peru**|231,434|0.18|***|-0.07||**Tunisia**|426,364|0.09|***|-0.07||
|**Dominican Rep.**|330,979|0.17|***|0.03||**Norway**|192,642|0.09|**|0.07||
|**Israel**|125,829|0.15|**|0.04||**Thailand**|168,634|0.09|***|-0.07||
|**Ecuador**|221,108|0.15|***|-0.10||**Finland**|316,196|0.06|*|0.16|*|
|**Mexico**|3,607,198|0.15|***|-0.10||**Greece**|938,753|0.06|**|0.00||
|**New Zealand**|242,460|0.15|***|-0.02||**Turkey**|1,733,521|0.05|*|-0.11|*|
|**Indonesia-East**||||||||||||
|**Timor-Maldives**|742,776|0.14|***|-0.03||**Germany**<br>**Belgium-**|2,840,813|-0.06|**|0.04||
|**Russia**|796,637|0.14|***|||**Luxembourg**|311,149|-0.06|**|-0.10||
|**Colombia**|723,293|0.14|***|-0.08||**Netherlands**<br>**France-Monaco-**|670,888|-0.08|**|-0.03||
|**Canada**|1,115,927|0.14|***|-0.21|***|**Andorra**|1,045,326|-0.08|***|-0.09||
|**Qatar**|1,586|0.12|*|-0.06||**Saudi-Arabia**|56,140|-0.09|*|-0.17|**|
|**Argentina**|253,322|0.12|**|-0.13||**Czechoslovakia**|1,014,715|-0.10|*|-0.18||

Superscripts ***, **, * denote statistical significance at 1, 5 and 10% respectively.

When unobserved pair-wise factors are accounted for, again the majority of these positive effects disappear. It was argued in the endogeneity section, that in theory, the direction of the bias from failing to account for the unobserved heterogeneity could work in either direction. This plays out, for example the point estimate for Finland increases from 0.06 to 0.16 following the inclusion of τij. Conversely, the coefficient for Canada falls from 0.14 to -0.21. However, the global and regional estimates suggest that overall; the unobserved bilateral factors are positively correlated with migrant networks on average, such that their inclusion dramatically removes most of the effect of migration upon trade. This story is again borne out by these results, since the vast majority of positive estimates are found to be insignificant or indeed significant and negative when fixed effects are included.

_11. Third Party Effects_ 27
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Note: vertical dashed line marks the start of the vaccination campaign
_Appendix Table 5: Characteristic balance at baseline: nonbeneficiary households in top quartile_ _<u>of saturated areas</u>_

|Baseline survey variables|Control<br>Mean|Difference<br>(Treated-<br>Control)|Standardized<br>Mean<br>Difference|p-value|
|---|---|---|---|---|
|LN Proxy Means Test Score|9.797|-0.013|-0.037|0.642|
|Household composition:|||||
|Household size|4.096|-0.742**|-0.189|0.025|
|Children 5 years old and below|0.521|-0.143*|-0.150|0.071|
|Children between 6 and 14 years old|0.877|-0.168|-0.106|0.196|
|Primary occupation: Farming and livestock|0.425|-0.037|-0.027|0.739|
|Educational attainment of the household head:|||||
|No grade completed|0.055|0.021|0.040|0.618|
|Some elementary school|0.247|0.049|0.046|0.566|
|Completed elementary school|0.247|-0.109***|-0.210|0.013|
|Some high school|0.082|0.019|0.031|0.701|
|High school graduate|0.192|-0.054|-0.082|0.311|
|Some college|0.110|0.020|0.032|0.692|
|College graduate|0.068|0.055|0.113|0.168|
|School Attendance:|||||
|Children between 6 to 11 years|0.882|0.090*|0.149|0.076|
|Children between 12 to 14 years|0.762|-0.081|-0.042|0.608|
|Housing Amenities:|||||
|Strong roof materials|0.507|-0.021|-0.017|0.834|
|Strong wall materials|0.466|-0.148*|-0.134|0.104|
|Light roof materials|0.233|-0.010|-0.009|0.908|
|Light wall materials|0.192|0.106|0.114|0.164|
|Owns a house and lot|0.315|0.063|0.069|0.392|
|House has toilet|0.192|0.167**|0.205|0.015|
|Shares a water source|0.288|-0.160**|-0.172|0.040|
|Household Assets:|||||
|Electricity in house|0.781|-0.072|-0.122|0.138|
|Owns a television|0.548|-0.084|-0.091|0.266|
|Owns a video recorder|0.466|-0.283***|-0.251|0.004|
|Owns a Stereo/CD player|0.411|-0.153*|-0.138|0.094|
|Owns a refrigerator|0.397|-0.144**|-0.185|0.027|
|Has a telephone/cellphone|0.274|-0.086|-0.077|0.345|
|Owns amotorcycle|0.192|-0.093|-0.098|0.227|

*** p<0.01, ** p<0.05, * p<0.1.

Estimates include municipality fixed effects. Standard errors clustered at the village level; there are 65 villages. Source: 2008 Household Assessment Form 54
different biodiesel blends with premium unleaded petrol, liquefied petroleum gas (LPG) and compressed natural gas (CNG). It can be seen that far greater reductions in NOx and PM can be achieved by shifting from diesel to these other readily available fossil fuels than by switching to biodiesel blends (Russi, 2008). Even though biodiesel can deliver better performance in terms of VOC than LPG, a 20% blend is necessary to surpass the performance of premium unleaded petrol and pure biodiesel is required to exceed the performance of CNG. These levels of blending are unlikely to be viable on a large scale.

**Table 6: Average Tailpipe Pollutant Emissions Compared to Low Sulfur Diesel**

||**NOx**|**PM**|**VOC**|
|---|---|---|---|
||(%)|(%)|(%)|
|**Biodiesel (100%)**|+10|-37|-76|
|**Biodiesel(20% blend)**|+3|-10|-24|
|**Biodiesel (5.75% blend)**|+1|-2|-4|
|**Petrol(premium unleaded)**|-88|-95|-9|
|**LPG**|-96|-95|36|
|**CNG**|-89|-94|-73|

Note: Emissions based on medium-sized passenger car. Source: Beer et al. (2004) and Morris et al. (2003) in Russi (2008).

Jacobson (2007) shows that a broad displacement of gasoline with high ethanol blends in the US would lead to greater emission of local air pollutants and negative health impacts from deteriorating air quality. Moreover, the cultivation of biofuel feedstocks can also affect local air quality through fugitive emissions of air pollutants. The frequent burning of cleared vegetation for biofuel production, as practiced in Brazil, Indonesia and Malaysia for example, causes severe smog and has adverse effects on human morbidity and mortality (Schwela et al., 2006). In addition, palm oil production may result in the fugitive emission of methane from water surfaces and liquid processing wastes (Yapp et al., 2008).

# _7.3. Impacts on Biodiversity and Ecosystems_

The effect of biofuel production on biodiversity depends on the type of land utilized. If degraded lands are restored for biofuel feedstock production, the impact could be positive. On the other hand, if peat lands are drained or natural landscapes are converted to biofuel plantations, the 31
|(8)<br>0.0112<br>(0.012)<br>0.0086<br>(0.007)<br>0.0090<br>(0.009)<br>0.0035<br>(0.004)<br>0.5582***<br>(0.065)<br>0.0289<br>0.221<br>11,718<br>0.690<br>Yes<br>Yes<br>Yes<br>0.0621<br>0.0368<br>mns 1-4), and (ii) the<br>g a syndicated loan<br>ountry-sector in the<br>ntry-sector received<br>ssociated p-value of<br> _<_0.1, ** p_<_0.05, ***|
|---|
|(7)<br>0.0201<br>(0.017)<br>0.0226**<br>(0.009)<br>0.0062<br>(0.013)<br>0.0053<br>(0.007)<br>0.5109***<br>(0.078)<br>0.0489<br>0.134<br>6,318<br>0.743<br>Yes<br>Yes<br>Yes<br>0.0954<br>0.0538<br>ate bonds (colu<br>e MDB providin<br>present in the c<br>al to 1 if the cou<br>and_t_, with the a<br>parentheses: * p|
|% GDP)<br>(6)<br>0.0080<br>(0.010)<br>0.0085<br>(0.006)<br>0.0038<br>(0.008)<br>0.0107<br>(0.008)<br>0.5550***<br>(0.061)<br>0.0203<br>0.279<br>13,869<br>0.693<br>Yes<br>Yes<br>Yes<br>0.0713<br>0.0396<br>mber of corpor<br>re is at least on<br>top banks was<br>a dummy equ<br>een year_t −_2<br>try level are in|
|Bond Size (<br>(5)<br>0.0082<br>(0.010)<br>0.0096<br>(0.007)<br>0.0049<br>(0.008)<br>0.5562***<br>(0.061)<br>0.0227<br>0.237<br>13,869<br>0.693<br>Yes<br>Yes<br>Yes<br>0.0713<br>0.0396<br>les are: (i) the nu<br>y equal to 1 if the<br>f at least 1 of the<br>ODA) we include<br>of MDBcs,t betw<br>lustered at coun|
|(4)<br>-0.0498<br>(0.213)<br>0.0681<br>(0.249)<br>-0.5706<br>(0.635)<br>0.0160<br>(0.073)<br>0.8958***<br>(0.061)<br>-0.552<br>0.451<br>11,718<br>0.898<br>Yes<br>Yes<br>Yes<br>0.0621<br>1.080<br>pendent variab<br>cs,t is a dumm<br>my equal to 1 i<br>nt assistance (<br>mulative effect<br>ndard errors c|
|(3)<br>0.1422<br>(0.282)<br>0.2141<br>(0.230)<br>-0.7618<br>(0.755)<br>0.1453<br>(0.230)<br>0.8046***<br>(0.092)<br>-0.406<br>0.672<br>6,318<br>0.905<br>Yes<br>Yes<br>Yes<br>0.0954<br>1.934<br>e bonds. The de<br>sector-year. MDB<br>e include a dum<br>fcial developme<br>om rows, the cu<br>me variables. Sta|
|Bonds<br>(2)<br>-0.0772<br>(0.257)<br>0.1187<br>(0.230)<br>-0.4171<br>(0.475)<br>0.0195<br>(0.095)<br>0.8966***<br>(0.057)<br>-0.376<br>0.585<br>13,869<br>0.904<br>Yes<br>Yes<br>Yes<br>0.0713<br>1.331<br>on 1 on corporat<br>) in the country-<br>top 10 banks, w<br>fnancing and of<br>ports, in the bott<br>DBcs,t and outco|
|Number of<br>(1)<br>-0.0768<br>(0.257)<br>0.1208<br>(0.227)<br>-0.4152<br>(0.473)<br>0.8966***<br>(0.057)<br>-0.371<br>0.584<br>13,869<br>0.904<br>Yes<br>Yes<br>Yes<br>0.0713<br>1.331<br>estimates of equati<br>GDP, columns 5-8<br> To control for the<br>or Chinese offcial<br>years. The table re<br>verages of the M|
|MDBcs,t<br>MDBcs,t-1<br>MDBcs,t-2<br>Top 10 Banks<br>Chinese lending<br>ODA<br>Number of Bondscs,t-1<br>Bond Size (%GDP)cs,t-1<br>2<br>∑<br>_k_=0<br>_MDB_<br>_Support_cs,t-k<br>_Wald test_ : _p value_<br>Observations<br>R-squared<br>Sector-country FE<br>Sector-year FE<br>Country-year FE<br>Average MDB<br>Average Dep. Var.<br>_Notes_: The table presents the<br>size of corporate bonds (% of<br>in country-sector_cs_at time_t_. <br>previous 2 years. To control f<br>these fows in the previous 2<br>a Wald test, and the sample a<br>p_<_0.01.|

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