rafmacalaba/datause-encoder-data huggingface dataset:
which is accommodated by changes in real consumption. The trade balance, then, drives the level of absorption. The consumer price index (CPI) is the model’s numeraire.

Finally, tax revenue losses due to tariff cuts are replaced: real aggregate revenue from all indirect taxes is kept fixed, via a uniform endogenous change in the power of indirect taxes on sales to households. This mechanism is equivalent to a lump sum tax, of value proportional to each household's spending<sup>16</sup> . It also mimics the traditional method of raising tax revenues in Brazil, through indirect tax collection.

# **5. Results**

## **5.1 The CGE model results**

The Brazilian economy has a limited exposure to external trade. The shares of exports and imports in total GDP were respectively 7.0 percent and 8.9 percent in the 1996 base year. These shares have increased recently, but not by enough to significantly change this picture<sup>17</sup> . Table 7 shows more information about the structure of Brazilian external trade as well as of related parameters and production structure, while Table 8 shows the nature and size of the shocks applied to the model.

As stated before, the shocks applied to the model were generated by a previous run of the GTAP model, where the Doha scenarios were implemented. The GTAP effects on the Brazilian

> 16 That is, neither the distribution of spending nor relative prices facing households are altered. With fixed labour supplies, distortion of any labour-leisure choice does not arise.

> 17 The share of imports plus exports in Brazilian GDP in 2001 and 2002 were, respectively, 22.3 percent and 23.4 percent.

16
is remarkable given the under-development of the services sector at the beginning of transition due to the primacy given to the manufacturing and agricultural sectors during the communist period. In fact, services represent a higher share of value added in the Slovak Republic, Russia, Estonia and Latvia than in EU-15 countries. Figure 1 shows some differences in the importance of services across ECA countries. While in the Baltic countries, Russia, and the Slovak Republic, services represent more than 50% of value added in 2001-2004, they represent only 38.5% in Macedonia. Also, services represent about 38% of employment in Croatia and Latvia but only 21.9% in Romania. In general, services contribute higher shares to value added and employment in EU-8 countries than in other ECA countries, with Croatia being an exception in terms of employment.

**_Figure 1: Services Sector Share in Value Added and Employment_**

<!-- Start of picture text --> Panel A. Share in Total Value Added Panel B. Share in Total Employment<br>Latvia EU-15<br>Estonia Latvia<br>Russia Croatia<br>Slovak Rep. Estonia<br>EU-15 Hungary<br>Bulgaria Slovenia<br>Poland<br>Czech Rep.<br>Lithuania<br>Poland<br>Czech Rep.<br>Lithuania<br>Hungary<br>Slovak Rep.<br>Croatia<br>Serbia & Mont.<br>Slovenia<br>Ukraine Bulgaria<br>Romania Ukraine<br>Serbia & Mont. Macedonia<br>Macedonia Romania<br>0% 10% 20% 30% 40% 50% 60% 70% 0% 10% 20% 30% 40% 50%<br>1997-2000 2001-2004 1997-2000 2001-2004<br><!-- End of picture text --> Source: WIIW database 2005 and KLEMS database 2007. Note: The data for Baltic countries and for EU-15 countries is taken from the KLEMS database 2007.

Evidence on the importance of specific services sub-sectors across ECA countries is provided in Figures 2 and 3. Figure 2 shows the average share in total value added of each of the sub-sectors in the WIIW database in 1997-2000 and in 2001-2004. Note that a change over time in the share of a given sub-sector in value added in Figure 2 may depend on two opposing trends related to prices and quantities, which we are unable to disentangle. First, if production in a sub-sector increases by more than production in 5
<!-- Start of picture text --> d<br>L's<br>Bes yy . A phen, o> Jf 4 i a ~~<br>Yeof 2 ‘omy, ‘ ~ si eC Ser 7 hou. Af<br>Legend pany, yo Rie<br>c= Losses > -150.00 = 5 in<br>MMM -149 99--75 00 or : 4<br>HB -74 99- 50.00 \ a<br>GE ~19.99-24.99 - -25.00 rl yA<br>- 25.00 ntheJv<br>28.01 - $0.00 \ /<br>Wy 50.01 - 76.00<br>GE Gains >75 00<br><!-- End of picture text -->
_Table 8: Vulnerability and calorie consumption fixed-effect model_

||<br>|<br>Vulnerability|<br>|Per capita|<br>calorie intake|<br>per day (log)|
|---|---|---|---|---|---|---|
||1 Proaiit to<br>2|Hi vuneraiit|3 Moerate vuneraiit||5 Ration|6 Non-ration|
|Variables|y<br>poverty (p)<br>|g y<br>(p> 0.5)|y<br>(0.2 < P < 0.5)|(4) Total|<br>food|<br>food|
|Displaced with PDS|-0.100***|-0.072*|-0.125**|0.119*|5.731***|-0.198**|
||(0.031)|(0.041)|(0.054)|(0.062)|(0.429)|(0.085)|
|Observations|327|327|327|327|327|327|
|Adjusted R-squared|0.645|0.455|0.109|0.198|0.777|0.332|
|Governorate fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of clusters|91|91|91|91|91|91|
|Control mean(Displaced without PDS)|0.408|0.354|0.326|7.987|0|7.987|

Note: Standard errors are clustered at Nahiya (sub-district) level. Significance levels are denoted as follow: * p<0.10 ** p<0.05 *** p<0.01. Sample is limited to displaced households from the "long-form" sub-sample.

35
||Depen<br>(1)<br>Ln Height<br>in 2004|dent Variable<br>(2)<br>Years of schooling<br>in 2004|
|---|---|---|
|Ln per capita consumption (TZ shillings)|-0.006<br>(0.004)|0.456<br>(0.412)|
|Ln per capita consumption missing|-0.055|5.401|
||(0.043)|(4.968)|
|Dwelling has good flooring|0.010*<br>(0.006)|1.316***<br>(0.432)|
|Height of mother|0.002***<br>(0.000)|-0.016<br>(0.022)|
|Height of mother missing|0.330***|-3.857|
||(0.072)|(3.425)|
|Mother resides in household|-0.002|-0.780|
||(0.008)|(0.556)|
|Father resides in household|-0.003|-0.540|
||(0.005)|(0.343)|
|Number of observations|718|718|

27
Why are small- and medium-size firms more likely to become monopsonist than large firms? To understand this result, recall the first-order-condition in equation (7): _wit_ = _γit_ ( _Rit_<sup>_L_).Itfollowsthatthedegreeofmonopsonypowerisgivenby</sup><sup>_γit_=</sup> 1+(( _ε_<sup>_L_</sup> _<u>wε</u>_<sup>_L_</sup> _w_<sup><u>)</u></sup><sup>_<u>it</u>_)</sup><sup>_it_where</sup> ( _ε_<sup>_L_</sup> _w_<sup>)</sup><sup>_it∈R_+is the wage elasticity of labor supply.Hence, if wages tend to be inelastic with</sup> respect to labor supply, then firms are likely to compress wages when faced with increased competition.

<!-- Start of picture text --> 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year Year<br>Small Medium Small Medium<br>Large Large<br>(a) (b)<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br>Year Year<br>Small Medium Small Medium<br>(c) (d)<br>400<br>250<br>200 300<br>150<br>200<br>100<br>Employment level Real Wage Index<br>100<br>50<br>0 0<br>25 160<br>140<br>20<br>120<br>15<br>100<br>Employment level Real Wage Index<br>10<br>80<br>5 60<br><!-- End of picture text --> Figure 4: Trends in Employment Level and Real Wage

**_Source:_** Author’s analysis based on data from the World Bank Regional Project on Enterprise Development (RPED) and Ghana Manufacturing Survey (GMES) from 1992 to 2003.The surveys were conducted by the Centre for the Study of African Economies (CSAE) at the University of Oxford, University of Ghana, and Ghana Statistical Service. **_Note:_** Panels (a) & (b) present trend in average employment and real wages for small, medium, and large firms. Employment data is reported in levels while real wage data is indexed with 1991=100. Panels (c) & (d) repeats the analysis in Panel (a) & (b) respectively for only small and medium firms.

Figure 4 shows the trend in average employment levels and real wages across the three categories of firm sizes.<sup>14</sup> It can be observed from panel (a) that large firms increased their average employment level over the decade. On the other hand, the average employment level for small and medium firms stayed almost constant. Panel (b) of the same figure shows an increase in the real wage with relative to the base year for large firms.

Panels (c) and (d) of fig. 4 show the same plots as in panels (a) and (b) but omit 19
|November|(0.291)<br>-0.135***|(0.371)<br>-0.0719***|
|---|---|---|
||(0.000)|(0.000)|
|Missing|3.001***|3.888***|
||(0.000)|(0.000)|
|**Product bundling, Baseline: 1-3 items**|||
|4-8 items|-0.550***|-0.512***|
||(0.000)|(0.000)|
|9-14 items|-0.460***|-0.374***|
||(0.000)|(0.000)|
|15-22 items|-0.575***<br>|-0.479***<br>|
||(0.000)|(0.000)|
|23-27 items|-0.400***<br>(0.000)|-0.297***<br>(0.000)|
|28-35 items|-0.361***<br>(0.000)|-0.307***<br>(0.000)|
|36-38 items|-0.400***|-0.382***|
||(0.000)|(0.000)|
|39-50 items|-0.599***|-0.420***|
||(0.000)|(0.000)|
|51-111 items|-0.386***|-0.431***|
||(0.000)|(0.000)|
|112-154 items|-0.331***|-0.343***|
|**Quantity ofpurchasedgoods, Baseline: 1-24**|(0.000)|(0.000)|
|Purchasedquantity=25-200|-2.629***|-2.661***|
||(0.000)|(0.000)|
|Purchasedquantity=201-582|-3.497***|-3.491***|
||(0.000)|(0.000)|
|Purchasedquantity=583-3000|-4.130***|-4.073***|
||(0.000)|(0.000)|
|Purchased quantity=3001-10000|-4.363***|-4.397***|
||(0.000)|(0.000)|
|Purchased quantity=10001-39110|-4.696***|-4.719***|
||(0.000)|(0.000)|
|Purchasedquantity=39111-100000|-5.175***|-5.156***|
||(0.000)|(0.000)|
|Purchasedquantity=100001-319146|-5.515***|-5.543***|
||(0.000)|(0.000)|
|Purchasedquantity=319147-1000000|-5.996***|-6.065***|
||(0.000)|(0.000)|
|Purchasedquantity>1000000|-6.760***|-6.904***|
||(0.000)|(0.000)|
|**Supplier specialisation, Baseline:**1-8 markets|||
|Supplier is on 9-14 markets||-0.0291**|
|||(0.009)|
|Supplier is on 15-52 markets||-0.367***|
|||(0.000)|
|Supplier is on 53-69 markets||-0.671***|
|||(0.000)|
|Supplier is on 70-86 markets||-0.904***|
|||(0.000)|
|Supplier is on 87-98 markets||-0.750***<br>|
|Supplier is on 99-109 markets||(0.000)<br>-1.005***|
|||(0.000)|
|l k||***|
|Suppier is on 110-116 marets||-1.114<br>(0.000)|
|Supplier is on 117-123 markets||-1.317***|
|||(0.000)|
|Supplier is on 124-139 markets||-1.243***|
|||(0.000)|

24
**Table 2: Sizes of Privatized and Residual Entities**

|Bank|Privati|zed Entity|Residu|al Entity|% Tran<br>Privat|sferred to<br>e Owner|Resi|dual|
|---|---|---|---|---|---|---|---|---|
||Assets<br>(million<br>pesos)|Liabilities<br>(million<br>pesos)|Assets<br>(million<br>pesos)|Liabilities<br>(million<br>pesos)|Assets<br>(million<br>pesos)|Liabilities<br>(million<br>pesos)|Asset<br>recovery<br>(mil)|As % of<br>Assets|
|Chaco|42.9|34.5|245.3|233.1|15%|13%|0*|0%|
|Entre Rios|425.5|414.5|0.0|0.0|100%|100%|--|--|
|Formosa|26.5|11.5|135.7|244.9|16%|4%|5.2*|4%|
|Misiones|67.2|57.8|133.9|340.8|33%|14%|6.1|5%|
|Rio Negro|59.4|47.4|379.2|402.6|14%|11%|6.0|2%|
|Salta|42.9|41.0|70.0|68.4|38%|37%|17.6*|25%|
|Tucuman|66.9|56.9|261.7|262.9|20%|18%|5.3|2%|
|San Luis|38.6|38.6|29.7|81.8|56%|32%|1.7|6%|
|Sant. Estero|43.8|43.5|199.6|227.3|18%|16%|7.5|4%|
|San Juan|173.9|158.9|78.6|175.3|69%|48%|2.3|3%|
|Mendoza'|335.1|326.9|666.6|666.6|33%|33%|14.3|1%|
|Prev. Social|62.9|41.0|292.1|292.1|18%|12%|--|--|
|Mun. Tucuman|38.2|32.2|38.1|25.7|50%|56%|n.a.|n.a.|
|Jujuy|35.7|33.7|206.9|218.7|15%|13%|n.a.|n.a.|
|Santa Fe|--|--|--|--|--|--|||
|Santa Cruz|157.9|142.9|37.7|126.9|81%|53%|n.a.|n.a.|

Source: Fondo Fiduciario.

* Province also refinanced some (less than ten percent) of residual assets. Refinancings are not included in the recovery figures presented here.

# **III. A Simple Model of Bank Privatization**

# **A. Conceptual Framework**

Our basic premise is that political and economic constraints dictate the timing and design of bank privatizations, and that timing and design, in turn, affect post-privatization performance. This will help us understand the second link in the framework, which will focus on how political and economic constraints affect features of the privatizations (including privatization contracts). We begin our study of privatization contract features with a simple theoretical model. We will then use the sample of Argentine bank transactions to test our predictions. Understanding the political economy of privatization decisions and contracts should also help us better assess the effect of these decisions on post-privatization performance.

12
were weighted by the fifth-grade enrollment at the school level. Table A.11 shows that with this strategy, we can control for the differences in learning trajectories between state and locallymanaged schools across municipalities, as the coefficient of _E × G × D_ is insignificant before the shutdowns (there is no significant difference in the learning gap of state and locally-managed schools in _G_ = 1 and _G_ = 0).

To summarize:

- _G_ = 1 are the 112 municipalities where the municipal governments did not extend the winter break, and where at least one state and one locally-managed school offering first to fifth grade. These municipalities have 929 state-managed schools with 99,172 students and 1,334 locally-managed schools with 139,017 students.

- locally-managed schools opened.

- state-managed schools closed.

- 1<sup>_st_</sup> difference-in-differences:

- in which _YE,G_<sup>1</sup> =1<sup>and</sup><sup>_Y_</sup> _E,G_<sup>0</sup> =1<sup>aretheproficiencyofstate-managedschoolsin</sup><sup>_G_=1</sup> in _D_ = 1 (2009) and _D_ = 0 (2007), respectively; and _YM,G_<sup>1</sup> =1<sup>and</sup><sup>_Y_</sup> _M,G_<sup>0</sup> =1<sup>arethe</sup> proficiency of locally-managed schools in _G_ = 1 in _D_ = 1 and _D_ = 0, respectively.

- _G_ = 0 are the 10 municipalities in which the municipal authority extended the winter break, and where there is at least one state and one locally-managed school. These municipalities have 868 state-managed schools with 144,655 students and 759 locallymanaged schools with 110,606 students.

- locally-managed schools closed.

- state-managed schools closed.

- 2<sup>_nd_</sup> difference-in-differences:

- in which _YE,G_<sup>1</sup> =0<sup>and</sup><sup>_Y_</sup> _E,G_<sup>0</sup> =0<sup>aretheproficiencyofstate-managedschoolsin</sup><sup>_G_=0</sup>

14
The estimates are practically unaffected by weighting each observation by the average employment in that local labor market (column 2). Clustering at the local labor market level reduces standard errors, since conditional on all the fixed effects immigration flows are negatively serially correlated (column 3).

The main concern with our instrument, as with a conventional Altonji-Card style shift-share instrument, is that there may be local labor market-specific long-term demand trends which affect both the historic and current distribution of immigrants (see also the discussion in Section 4.2, above). Our instrument, however, has the considerable advantage that the identifying variation comes from the interaction between a variable that varies over time (the size of an age group in a source country) and a variable that varies across local labor markets (the average propensity of workers of a specific age group and source country to be employed in that local labor market). Hence, unlike the Altonji-Card instrument which relies exclusively on cross-sectional variation, we can control for such correlated demand trends using industry-region specific linear trends. This is particularly important when using data for 1990 - 2010 since the instrument is no longer constructed using data for an earlier period, but within sample. Our point estimate of the causal effect of immigration on native employment in an industry-region remains positive and highly significant when including linear trends, see Panel A.2. We would argue that this provides very strong evidence that our main qualitative result (the scale effect is larger than the substitution effect) is not a result of an invalid instrument. Moreover, our point estimate is very similar to that obtained in our main specification using 2007-10 data, 0.37 compared to 0.41. This is consistent with the idea that having constructed part of the identifying variation of the instrument within sample when using 37
and outputs), or global demand and price ratios. If included, they are usually done as exogenous variables in CE or VAR in VEC; the CE deterministic regressors or additional deterministic regressors in FMOLS; or instrument variables in GMM.

Whatever the interventions, we further check the Johansen cointegration test summary and ensure that the chosen estimation has a cointegration equation in the case of VEC. In FMOLS, we look at tests such as Hansen Instability, Engle-Granger, etc., for confirmation. If the Ω estimation is the reduced form of the export system in equation 9, there could be up to two cointegration equations. No such proof is needed in the GMM case.

# IV. Estimates

Using the methodology described above, we estimated elasticities for as many as 191 countries.<sup>17</sup> Tables A1 and A2 in the appendix present the estimated elasticities, the method used, the t-tests, and a summary of the interventions introduced. Table A3 lists the data source and years covered for each country. The elasticities appear reasonable, and the t-tests (except for a handful) are significant. As the summary in Table 1 shows, one hundred twenty-six countries (66.0%) have the full sample size of 1970-2018; 153 countries (80.1%) have at least 30 years of observations in their data. Of the 38 cases (19.9%) with less than 30 observations, 58% (22 cases) are borderline, with 29 observations covering 1990-2018 and mostly former Soviet republics and allied communist states.

The estimates cover 45 of 48 countries in Sub-Saharan Africa, including low-income countries like Benin, the Republic of Congo, Niger, etc. Island economies of various sizes tally as many as 47, including Fiji, Kiribati, Marshall Islands, etc. The list includes seven microstates, economies with fewer than 1,000 residents, and 49 hectares of land, such as Nauru, Tuvalu, Liechtenstein, etc. Many of these countries have severe data constraints that would have made estimation difficult with more elaborate formulations and data requirements.<sup>18</sup> Table 1: Sample size distribution in the estimation

|Sample Size|No. of countries|Percent distribution|
|---|---|---|
|49(full sample)|126|66.0%|
|40 - 48|8|4.2|
|30 - 39|19|9.9|
|20 - 29|38|19.9|
|Total|191|100.0%|

Source: Authors' calculation. Note: Full sample = 1970-2018.

> 17 The terms – countries, territories, and economies – are used interchangeably. We include any that have national accounts data in the WDI and UN sources.

> 18 Even so, it was not possible to estimate elasticities of some countries. A few of these are countries that have experienced severe conflicts – such as Afghanistan, Somalia, Sudan, and South Sudan. For some small countries - like Cura ç ao, Monaco, New Caledonia, Palau, Nepal etc., flat relative prices are registered for an extended or entire period. In those instances, the countries employed exactly the same implicit price indices for GDP, exports, imports, and (therefore) domestic goods. Hence, the estimated elasticities were essentially zeros. These countries were excluded.

14
Following a year in which there was no new syndicated lending for infrastructure sectors, the $5.1 billion in syndicated lending to South Africa in 2006 dwarfed the corresponding amount loaned to the 23 other countries. This lending comprised two exceptionally large transactions, totaling $4.6 billion, for two South African companies in the cellular telecommunications sector. Mobile Telephone Networks, Ltd. borrowed $3.5 billion (of which $2.5 billion was U.S.-dollar-denominated, the remainder Randdenominated) in a multitranche facility, with maturity terms ranging from one to five years. The loan was used to support the $5.5 billion acquisition of Investom and to refinance a bridge loan taken out previously the same year. The other large loan, a Rand-denominated $1.1 billion, five-year term loan, was borrowed by Vodacom Group for general corporate purposes.

In Chile and Malaysia, transport infrastructure received 36 percent ($1.8 billion) and 20 percent ($476 million), respectively, of total syndicated lending to infrastructure borrowers. Electricity generation ranked second in Chile as a destination for syndicated lending to infrastructure sectors; it received 34 percent of the total in 2006, followed by telecommunications, which received 22 percent. Telecommunications, driven by mobile-phone service providers, received just over half, or $1.2 billion, of syndicated lending to infrastructure in Malaysia.

Of the 23 other focus countries, Nigeria was the top destination country for loans financing infrastructure development in 2006 (table 3.3), borrowing $890.6 million. Of this, $680 million went to Bonny Gas Transport, in two tranches of 12 and 12.5 years, for construction of a liquefied natural gas train. The other $210.6 million went to a four-tranche loan for United Cement Co. (UNICEM), with maturities ranging from four to nine years, toward construction of a 47MW power plant in Calabar, Nigeria. Kenya ranked second as a destination in 2006. Its $181.9 million loan went to borrowers in telecoms (Safaricom, $165.1 million) and electrical utilities (Iberafrica Power, $16.8 million). Zambia was the third destination country for a syndicated lending to infrastructure development in 2006. There, cellular telecoms company Celtel borrowed $105 million for capital expenditure purposes.<sup>21</sup>

> 21 Sudan (Al Manara Water Co.) and Mozambique (Fundo do Investimento e Patrimonio do Abastecimento de Agua, FIPA) attracted a further $93.3 million and $38.8 million, respectively, in bilateral loan deals tracked by Dealogic, for financing of a water treatment plant (Sudan) and upgrading and expansion of water supply services within the Greater Maputo metropolitan area (Mozambique).

23