rafmacalaba/datause-encoder-data huggingface dataset:
determinants related to investment. Also, the author conclude that even keeping unchanged the actual level of poverty, reductions in regional inequality will require reallocation of industrial activity to peripheral regions.

And, finally, the same author also concludes that opening of the economy to the external market (mainly in relation to the formation of Mercosul) would help reduce regional inequality in Brazil. This would happen through reduced consumer prices in the poorest regions, which are fortunately lacking in the industries most threatened by new trade flows.

The behavior of wages and the allocation of labor throughout the 1980-99 trade liberalization period in Brazil was analyzed by Green _et al_ (2001). Among the main findings the authors point out that wage inequality remained fairly constant for the 1980s and 1990s, with a small peak in the mid 80s. The main conclusion of the study is that the egalitarian consequences of trade liberalization were not important in Brazil for the period under analysis. As caveats, the authors note the low trade exposure of the Brazilian economy (around 13 percent in 1997), as well as the low share of workers that have completed college studies in total (1 in 12 workers at that time).

Gurgel, Harrison, Rutherford and Tarr (2003) present a CGE analysis of the effects on Brazil of trade liberalization. They use a GTAP-derived multi-country model with additional Brazilian detail. For Brazil, 10 urban and 10 rural household income types are recognized. The paper compares the effects of the FTAA, EU-MERCOSUL, and multilateral trade agreements on Brazil. Amongst variant scenarios are: the effects of FTAA or EU-MERCOSUL if the USA/EU did _not_ offer free access to Brazilian farm products; and the interaction _between_ trade deals, eg, whether FTAA makes EU-MERCOSUL less attractive. They conclude that the trade deals are in varying degrees good for Brazil, and especially good for Brazil's poor. The poor benefit more 4
The problem of cost escalation seems to be endemic to the nuclear industry and not limited to the construction experience in the United States. The French nuclear program has often been touted as the most successful nuclear scale-up that achieved economies of standardization that eluded the U.S. industry. However, recent analysis suggests that the French program too has been characterized by a substantial escalation of real-term construction costs (figure 6). And that radical design changes were the primary cause for this cost escalation. Interestingly, the prime motivation for these cost-enhancing design changes does seem to have been improved safety imposed by increased regulatory scrutiny. Instead, these non-safety design changes were driven by the desire for larger scale, higher domestic value-added for the nuclear industry, and more output (Grubler, 2010).

**Figure 6 Investment costs of French PWRs (per kW yearly averages) over time**

<!-- Start of picture text --> 1000 FF (1998) /kW<br>16<br>12<br>8<br>4<br>0<br>1970 1975 1980 1985 1990 1995 2000<br><!-- End of picture text --> _Source_ : Grubler (2010).

The industry also has a notoriously poor historical record on construction cost estimation, realization and time to build. Indeed, the construction of most nuclear plants around the world has been plagued with substantial cost overruns. In the United States, for example, the final costs of plants that began commercial operations in the late 1970s were in some cases several times greater than their initial cost estimates. For the 75 nuclear power plants that were constructed between 1966 and 1986, the average actual cost of construction exceeded the initial 17
The first factor is the rapid rise in wages and fringe benefits such as housing, education, health care, and retirement pensions (Sachs, Woo, 1994, Gordon and Li, 1997). Wages have generally risen at a slightly slower pace than output growth<sup>6</sup> reflecting strong labor demand by the nonstate sector. Previous studies argued that the increasing autonomy of SOEs in the absence of private property rights has led to excessive workers remuneration. Specifically, in their enterprise sample Woo et al. (1994) report a large increase in the share of labor incomes through valueadded. A study by Fan and Schaffer show that the increase in government subsidies is due to a faster increase in wages, fringe benefits and other unauthorized expenditures (Fan an Schaffer 1991). The social obligation of SOEs is not a new phenomenon. However, in recent years the cost of providing social welfare has risen fast.<sup>7</sup> There are two main reasons for this rapid increase during the 1990s: aging population, and rising medical costs. As the proportion of employed labor shrinks and the number of pensioners rises, SOEs are forced to draw on current revenue to fund spending on increasingly expensive social welfare.<sup>8</sup> Data from China Yearbook shows that the share of insurance and social welfare funds" in the state sector has risen from 17% in 1980 to 33% of total wage bill in 1997 (if we include employer subsidies to housing and the cost to enterprise of dealing with redundant workers, estimated ratio could be up to 75% of total wage Two possible reasons: first, the Chinese governuent has been able to maintain substantial implicit taxes and transfers among workers in the state sector, implemented through direct controls over relative wage rates. Second, lack of influence of labor union over wage rates (Gordon and Li, 1997) 7For example, the social welfare bill increased from 11.6 billion in 1980 to 257.8 billion in 1997, a rise of 182 percent at current prices.

8 The World Bank "China 2020" reports that the ratio of workers to pensioners is projected to decline from 10 to I in 1995 to about 3 to I in 2050.

5
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Poiiy RESEARCH WORKING PAPER 1450

# Summary findings

Using data from the 1992 Bulgarian household budget survey, Hassan and Peters analyze the structure of income in Bulgaria, identifying who the poor are and how they are reached by the social safety net. Their main findings about household incomes:

* Social transfers provide an extremely large - - component 24 percent of household income per capita. That is roughly on a par with the share in other Eastern European counitries but more thari 40 percent higher than the share in OFCL) countries.

* Wage earnings have declined as a source of income, reflecting the contraction of the state sector. Wage income as a share of income in Bulgaria has declined to only half the OEC.D level.

* Income from self-employmenit has increased, reflecting the surge in small-scale retail establishments.

* Income is considerably less concentrated in Bulgaria than in other lower-middle-income countries.

The authors' main findings about rhe poor (the bottom 20 percent in terms of household income): The head of household in a poor home tends to be older, a woman, poorly educated, and unemployed. Poor households are not necessarily larger households in Bulgaria, unlike in other developing countries. rhe sources of income in poor Bulgarian households reflect other findings: _*_ The poor depend for more than half their income on social benefits (especially pensions), indicating the importance of the social safety net.

The social safety net is not well targeted. Most social benefits are pro-poor, in **the** sense that they improve income distribution, but many benefits accrue to better-off households. There is substantial scope for better distribution of income.

Hassan and Peters conclude that comprehensive reform of social benefits is needed, focusing on pensions, unemployment benefits, child allowances, and social assistance.

- - This paper a product of the Country Operations Division, Europe and Central Asia, Country Department I is part of a larger effort in the department to analyze the social dimensions of stabilization and adjustment. Copies of the paper are available free from the World Bank, 1818 H Street N`W, Washington, DC 20433. Ilease contact Faith Smith, room _H5-245,_ extension 36072 (34 pages). Mav 1995.

_The Policy Research Working Paper Series dissenminates the findings ot work_ **_in_** _progress to encourage the exchange of ideas about development issues. An ob;ectwile of the series is to get the findings ouit quicklB, even if the presentations are less than fully polished. The papers carry the namnes of the authors and sho)uld be used and cited accordingly. 'I'he findings, interpretations, and conclusions are the authors' own and should no t be attributed tt, the World Bank,_ **_its_** _Execurive Board of Directors, or any of its member countries._ 1'rodt ced bV the Policv Rsearclh D)issemination Center
# **3. Estimating the Pass-Through Effect of a Nominal Exchange Rate Devaluation**

This section aims at estimating the exchange rate pass-through to domestic prices that would be necessary for the subsequent analysis of the social and fiscal impact of a nominal exchange rate devaluation.

Drawing on available time series data, the following equation is estimated:<sup>10</sup> where _pit_ is the log price index of good _i_ , _neert_ is the log effective nominal exchange rate and _pit_ * is log effective foreign producer price index for good _i_ and ε is the white noise disturbance term. The terms _Ai_ ( _L_ ) , _Bi_ ( _L_ ) and _Ci_ ( _L_ ) denote lag polynomials. The effective foreign producer price is computed as arithmetic averages using trade weights<sup>11</sup> : Where _Pit_ * is the effective foreign producer price index, _wn_ is the average trade weight for Djibouti’s main trading partners between 1995 and 1999, and _P_ int* is the producer price index of foreign country _n_ at time _t_ .

The short-run exchange rate pass through elasticity is defined as the contemporaneous coefficient of _Bi_ ( _L_ ) while the long-run elasticity is _Bi_ ( _L_ ) /(1 − _Ai_ ( _L_ )) . Thus a 1 percent nominal effective devaluation will result in an increase in relative prices of the good in question by _Bi_ ( _L_ ) /(1 − _Ai_ ( _L_ )) .

Due to data limitations, pass-through equations could not be estimated for all the tradable goods. For the purposes of this paper, a single pass-through equation is estimated on food prices using available using quarterly data from May 1999 to March 2005<sup>12</sup> . Food staples constitute the largest share in the CPI and in the average household budget. Food represents about 43 percent of overall household expenditure. The combined food and qhat share of an average household’s budget makes up nearly four-fifths of the share of tradable goods in the overall household budget. Since data on qhat prices is unavailable, qhat is assumed to have the same pass-through elasticity as food.

> 10 Adapted from Campa and Goldberg (2002)

> 11 Foreign PPI were estimated for the following trading partners: France, Italy, United Kingdom, Thailand and Japan, drawing on data from WEO (2004).

> 12 Aggregated quarterly data is available from May 1999 to March 2005 on the following categories of items in the CPI: foodstuffs, drinks, tobaccos, clothing and shoes, housing, water, electricity, gas and other fuels, furnishing, domestic equipment and servicing house, health, transportation, leisure and culture activities, hotels, cafés and restaurants and other goods and services

10
Table 6.4—Regression results of migration (linear probability model), for full sample and trimmed and weighted sample

|||Full|Sample||Trimm|ed and W|eighted Sampl|e|
|---|---|---|---|---|---|---|---|---|
||Model 1||Model 2||Model3||Model 4||
|TEEP cohort*TEEP province|0.222|(1.60)|0.149|(1.07)|0.244*|(1.65)|0.137|(0.94)|
|Female|0.394***|(3.71)|0.400***|(3.75)|0.365***|(3.11)|0.375***|(3.30)|
|Female*TEEP cohort|-0.0369|(-0.95)|-0.0373|(-0.97)|-0.0111|(-0.26)|-0.0131|(-0.32)|
|Female*TEEP cohort*TEEP province|0.0607*|(1.91)|0.0608*|(1.91)|0.0524|(1.51)|0.0514|(1.51)|
|Log(consumer durables)|0.00720|(0.62)|0.00282|(0.23)|-0.00173|(-0.11)|-0.00144|(-0.09)|
|TEEP cohort|0.0149|(0.09)|-0.0168|(-0.10)|-0.0383|(-0.20)|0.00651|(0.03)|
|TEEP cohort*Log(consumer durables)|0.00321|(0.25)|0.00766|(0.55)|0.00780|(0.48)|0.00625|(0.36)|
|Female*Log(consumer durables)|-0.0250***|(-2.85)|-0.0254***|(-2.89)|-0.0239**|(-2.43)|-0.0246**|(-2.56)|
|TEEP cohort*TEEP province*Log(consumer<br>durables)|-0.0277**|(-2.24)|-0.0210*|(-1.72)|-0.0276**|(-2.09)|-0.0223*|(-1.70)|
|Age|-0.111|(-1.52)|-0.101|(-1.34)|-0.128*|(-1.71)|-0.131*|(-1.85)|
|Age squared|0.00360*|(1.81)|0.00334|(1.63)|0.00417**|(2.03)|0.00429**|(2.20)|
|Mother’s years of schooling|-0.00562**|(-2.11)|-0.00554**|(-2.09)|-0.00461|(-1.66)|-0.00456|(-1.63)|
|Mother’s age|0.000941|(1.01)|0.000892|(0.96)|0.00129|(1.11)|0.00128|(1.11)|
|Number of siblings|0.0114***|(3.97)|0.0114***|(3.94)|0.00970***|(2.72)|0.00956***|(2.65)|
|Poverty incidence rate|||0.00579**|(2.23)|||0.00395|(1.34)|
|Infrastructure index|||-0.0486|(-0.61)|||-0.0523|(-0.68)|
|Number of students per classroom in high<br>school|||-0.000256|(-0.57)|||-0.000963|(-1.62)|
|Other variables and school fixed effects|yes||yes||yes||yes||
|Percentage of predicted values out of [0,1]|6.3%||8.0%||7.6%||9.2%||
|Number of observations|2,962||2,962||2,761||2,761||

Source: TEEP Tracking Survey. Notes: Other variables include house value in 2000, residential land value in 2000, commercial land value in 2000, and agricultural land value in 2000.

t values are in parentheses.

* significant at the 10% level; ** significant at the 5% level; *** significant at the 1% level.

40
item will be included: "Adjustment not computed by the CB", amounting to R$6,646 millions

|TABLE III.7. PUBLIC SECTO|R EORRO|WING RE|QUIRE|MENTS:19|95-1998|
|---|---|---|---|---|---|
|In R$|1995|1996|1997|1998|Accumulated|
|Nominal|48,650|45,741|53,232|72,375|219,999|
|Federal Government and CB|15,632|19,946|22,912|49,351|107,841|
|States and Municipalities|24,141|21,076|26,377|18,416|90,010|
|State Owned Enterprises|8,877|4,720|3,943|4,608|22,148|
|Nominal Interest|51,065|45,001|44,923|72,492|21,3481|
|Federal Government and CB|19,554|22,854|20,537|54,485|117,430|
|States and Municipalities|22,992|16,840|19,941|16,570|76,343|
|State Owned Enterprises|8,519|5,309|4,444|1,437|19,709|
|Primary|-2,415|740|8,309|-116|6,518|
|Federal Government and CB|-3,922|-2,908|2,375|-5,134|-9,589|
|States and Municipalities|1,149|4,236|6,436|1,846|13,667|
|State Owned Enterprises|358|-589|-501|3,172|2,440|

After all these adjustments, the equation which links the federal debt variation (+ Central Bank) with the federal nominal deficit is expressed on Table 111.8. It shows that the largest share of item #1, which can be identified with the financing of the federal public debt (+ CB), was due to interest payments (71.0%). Item #1's second biggest expansion source was 21
# **4 Data Analysis**

The primary source of our data is the Census Bureau. We use employment data in March 2007 and March 2010 from the County Business Pattern (CBP) dataset, given that these dates represent the lowest and highest points of the U.S. unemployment rate during the Great Recession. This data comes with flags representing employment ranges, which we replace with average employment values. We use the NAICS codes obtained from the CBP dataset at the sixdigit level for supporting industries and at the four-digit level for tradable employment as provided by data from Mian and Sufi (2014). In later parts of the paper, we also use employment data by firm size from the CBP dataset in order to examine potential problems related to credit.

What classifies an industry as tradable or supporting? For the purposes of this paper, we follow Mian and Sufi (2014)’s classification of the tradable sector based on global trade data: a 4-digit NAICS industry is defined as _tradable_ if it has imports plus exports equal to at least $10,000 per worker, or if total exports plus imports exceed $500M. Regarding the supporting industries, we base our categorization of a NAICS code on whether the industry aids the tradable sector, be it through local or regional support. In order to be identified as a supporting industry for our purposes, the given industry should support tradable industries, but it cannot also deal with the residential population. In determining the suitability of an industry, we examine each NAICS code provided on the NAICS Association’s website (www.naics.com). Table 3.2 presents a full listing of supporting industries. A more detailed description of this methodology is provided in the Appendix.

A factor prevents us from using the input-output table. We use six-digit NAICS codes to classify our industries, whereas the input-output table provided by the Bureau of Economic Analysis (BEA) mainly uses NAICS codes up to two or three digits. For example, the BEA 11
quality may require a substantial increase in costs, which would eventually be translated into higher fares. After considered these arguments, it remains important to reconcile the establishment and implementation of indicator reference standards. Table 5.4. gives some examples of indicators and their associated standards.

**Table 5.4. Examples of reference standard levels**

|Financial|Return on capital investment|> 1.0|
|---|---|---|
|LAbor prou-tvit|r<br>yZ|20P0<br>to|
|Service quality|[Numberof square meters per occupant at<br>jpeak hours|_25-35_(international)<br>1 6-20 (national)|

Source: Adapted form various studies.

Doganis and Graham (1995) have carried out evaluations of the economic and commercial aspects of 25 European airports through the application of a set of performance indicators.<sup>3</sup> ' The authors emphasize the existence of comparability problems due to differences in the activities developed at airports in the study. They try to lessen the problem through corrections aimed at allowing for a consideration of the whole group as operators of the same activities.<sup>32</sup> The sample includes private airports such as Glasgow, partially privatized ones such as Copenhagen, publicly owned but commercially oriented airports such as Geneva, and airports like Stockholm, which is part of the Swedish Civil Aviation Authority. The main objective of this study was to analyze the trends and development of industry performance, and identify the relationship between profitability and type of airport. Airports with different ownership structures and varying sizes were incorporated into the study. Table 5.5 provides a summary of the results of this study.

Finally, it should be pointed out that differences in the type of services developed in airports, such as in the degree of public intervention, accounting systems, financial sources, subsidies and standards, complicates the use of indicators for assessing airport performance. In addition, there are two important features that affect airport operations and are present in the evaluation of the resources applied. The first concerns the impossibility of storing airport output, which inevitably leads to a lessening in capacity The second feature has to do with airport externalities. For instance, the level of noise might induce a ban on night activities, which would lead to under-capacity use and an,increase in average costs. All these elements, combined with the geographic, economic, social and political characteristics of the airport region, hinders airport performance assessment.

> 3 Airports included in the study were: Amsterdam, Stockholm, Barcelona, Birmingham, Bilbao, Basel Mulhouse, Copenhagen, Cardiff, Dublin, Dusseldorf, East Midlands, Oslo, Frankfurt, Glasgow, Geneva, Gatwick, Heathrow, Lisbon, Madrid, Manchester, Milan, Nice, Newcastle, Vigo and Vienna.

> 3 Adjustments carried out in the study indicate that results must be carefully analyzed. Comparability problems are still present, and consequently each airport should be considered according to its context.

53
in the market, contributing to the sluggish growth of aggregate productivity. Cavalcanti et al. (2021) present further evidence on rm dynamics using data from Relação Annual de Informações Sociais (RAIS), a matched employer-employee administrative dataset covering all formal rms in Brazil that follow rms and workers over time. They nd that rm size in Brazil is increasing but concave in age over the rst 15 years of rm life, on average rising 50 percent relative to its entry size. This growth is signicantly less than the lifecycle growth for manufacturing plants reported by Hsieh and Klenow (2014) for the U.S., which show 8-fold average growth over 30 years, but greater than the roughly 1.25-fold increase reported for India. Ulyssea (2020a) analyzes both formal and informal rm dynamics in Brazil, combining rm-level data with a structural model. The paper documents that in, both sectors, rms display an increasing and concave age-size prole. However, the growth in size is signicantly higher for formal rms. The results show that after 10 years there is a 50 percent growth in the sample that includes only formal rms and their formal workers in contrast with the 20 percent average growth that one obtains when using both formal and informal rms. The author shows that the age-size prole using only formal rms and workers in Brazil is very similar to the one documented for Mexico by Hsieh and Klenow (2014). However, once he incorporates informal rms, the age-size prole of Brazilian rms becomes much closer to that of Indian rms, which is remarkably atter. These facts show that dynamic selection takes place in both sectors but is substantially weaker in the informal sector. They also suggest that the lack of dynamism found in the Indian data might also be present in other highinformality countries. Failing to incorporate informal rm dynamics may therefore lead to a substantial underestimation of the lack of dynamism among developing countries' rms.<sup>14</sup>

# 4 Competitive distortions and productivity

There are signicant barriers to competition in Brazil, many of them policy-induced. This section will discuss the evidence on the eect of several competitive distortions on productivity in Brazil.

## 4.1 Credit subsidies, nancial frictions and lack of competition in credit markets

In the presence of nancial market imperfections, the process of ecient allocation of resources can break up. Financial frictions can reduce aggregate productivity via two chan-

> 14See Akcigit et al. (2021) for an analysis of the dierences in the competitive selection mechanism between India and the United States and their aggregate implications.

14