rafmacalaba/datause-encoder-data huggingface dataset:
partially explained by changes in the composition of employment across sectors, occupations, and the formal or informal status of the worker.

Third, the study provides compelling evidence that the evolution of wage compression across firms is an important driver of wage inequality dynamics among workers in the formal sector. Using matched employer-employee data from Brazil, Costa Rica, and Ecuador, it decomposes the evolution of the wage variance into four components: changes across workers, changes across firms, changes in the assortativity between workers and firms, and a residual. The results show a prominent role of changes in the dispersion of wages across firms.<sup>4</sup> In Costa Rica, where inequality increased, the firm fixed-effects component grew the most. In Brazil and Ecuador, where inequality fell, the firm fixed-effects component fell the most.<sup>5</sup> Moreover, in Brazil and Ecuador, inter-firm differences in pay for similar workers fell, contributing (more than intra-firm dynamics) to less inequality. The important fall in between-firm wage inequality in Brazil and Ecuador is in sharp contrast with results for developed economies where, just like in Costa Rica, between-plant wage inequality has grown over time. Overall, these results highlight the importance of considering firm heterogeneity when examining the drivers of wage inequality in the region.

How could wage inequality decline in Latin America when global forces, in particular skillbiased technical change and job polarization, are increasing the dispersion of wages elsewhere (Autor, Katz and Kearney 2008; Goos, Manning, and Salomons 2009)? This paper assesses changes in employment and wages across detailed occupations in Brazil, Chile, Mexico, and Peru and finds little evidence of job polarization, perhaps with the exception of Chile. Wages expanded rapidly in low-paying occupations relative to high-paying occupations, while technological advances that complement skill intensive occupations predict the opposite. There was no hollowing out in the wage distribution, because occupations with middle and high skill content expanded slowly and at a similar rate, while occupations with low skill content declined much faster. Evidence from other emerging countries also shows little sign of polarization (Maloney and Molina 2016). Thus, the absence of skill-biased technical change and job polarization has facilitated the decline of wage inequality in the region and raised the question of what other factors could have contributed to this trend, including changes in labor supply, commodity boom, trade shocks, formalization, and increasing minimum wages, as discussed in the last section of this paper.

> 4The analysis follows Abowd, Kramarz, and Margolis (1999) and Card, Heining, and Kline (2013).

> 5For Brazil, the analysis confirms for the overall formal sector workforce the findings of Alvarez et al. (2018), which focuses on prime-age males in this sector.

4
19 about banks. Instead, risk asset reviews (RARs) are needed to assess borrowers' financial condition, collateral values, portfolio risks under various scenarios, and the adequacy of provisions, complemented by an inventory of the human capital in the banldng system.'9 RARs help form an assessment of banks' ability to plan and to evaluate the risks they face, and can assist in changing their internal incentive systems; in effect, these reviews can be a form of technical assistance in training bank managers to think about and evaluate their business. In the limited cases in which risk asset reviews have been attempted, a typical finding is that internal systems are (often grossly) inadequate for an evaluation of the risk confronting banks, and RARs have been instrumental in establishing such systems. Highly and even mildly repressed banking systems, also typified by low levels of bank capital, will sorely need precisely this type of assistance.

These measures should not be interpreted as merely technical; they require substantial political will. In most cases they will uncover significant problems with the financial health of major borrowers, which are likely to call into question the existing interventions in the financial system. These measures also are likely to indicate problems in the intermediaries, including possible decapitalization of the system. This in turn will require govemment action to remedy the state of the financial institutions.

# ii) Restore and recapitalize?

Ordinarily, at this stage in the reform process the govemment must decide either to restore the financial health of banks with negative net worth or close them down. Inegenral, restoration efforts are not likely to pay off and the banks should be closed unless there are well-run banks as merger candidates or other sources of managerial expertise, or unless the needed institution building, mentioned above, is well underway. This point must be modified where doing so would involve essentially closing down the entire financial system (see Box 1). Restoration -- either through replacing bad loans with government bonds, merger with - another institution, or a combination of the two and recapitalization are recommended only when the resulting institution(s) will be less likely to make bad (nonperforming) loans than
The structure of poverty is clear in Pará: (a) Indians, blacks, and pardos are poorer than whites, (b) young households/household heads are poorer than older households/household heads, (c) the poor tend to work more in the informal sector, and (d) a greater share of those engaged in agriculture are poor as compared to industry or services. Furthermore, the deepest poverty is in rural areas, and among the poorly educated, among the blacks and Indians, and young household heads with children. Without interventions to improve their opportunities and assets, their plight is likely to worsen. The social protection measures described in Section 6 are particularly relevant for them.

|**Table 5.1:**||**Pove**<br>|**rty Pr**<br>**20**<br>|**ofile,**<br>**00**<br>|**Pará**<br>|<br>|<br>|
|---|---|---|---|---|---|---|---|
|||**P0**|**P1**|**P2**|**Pop. Shar**<br>**(Fk)**|**e**<br>**Contrib. to**<br>**Poverty**<br>**(Sk)**|<br>**Mean income**<br>**Spatially deflated**<br>**Sep/2002 values**<br>**(R$)**|
|**Headcount Ratio**|**(P0)**|**44.0**|**22.5**|**15.7**|**100.0**|**100.0**|**232.2**|
|**_Characteristics of_**|**_the Head_**|||||||
|**Gender**||||||||
||Male|44.8|23.3|16.2|77.4|78.8|234.7|
||Female|41.3|20.0|13.7|22.6|21.2|223.8|
|**Race**||||||||
||White|34.2|16.9|11.6|25.8|20.0|360.3|
||Black|50.8|26.8|19.0|7.2|8.3|162.4|
||Pardo|47.0|24.1|16.7|65.3|69.7|189.4|
||Asian|24.6|13.0|9.4|0.2|0.1|615.0|
||Indigenou|s<sup>60.6</sup>|42.3|35.7|0.6|0.9|128.1|
|**Age:**||||||||
||<25|52.7|28.5|21.0|7.7|9.2|148.2|
||25 to 45|47.4|25.1|17.7|50.1|53.9|215.0|
||45 to 65|41.9|21.3|14.7|31.6|30.0|274.0|
||>65|28.8|10.0|5.3|11.0|7.2|250.5|
|**Presence of peop**|**le aged**|||||||
||<5|59.8|31.8|22.0|39.4|53.5|135.4|
||5_15|54.8|28.4|19.4|57.1|71.1|159.7|
||15_24|47.3|23.7|16.1|57.4|61.6|195.7|
||>65|30.1|10.6|5.6|13.4|9.2|253.3|
|_Source_: Author’s|calculations bas|ed on C|ensus 2|000.||||

30
that unlike the aid volume data, one cannot generate annual fragmentation values, because about 60 percent of the activities included in AiDA lack project start and end dates. Indexes computed for any given subperiod require dropping all projects without start dates. Particularly for years before 1987<sup>13</sup> , such indexes will therefore be based on very incomplete project data. However, it is likely that fragmentation is fairly stable across countries over time; e.g. the fragmentation indexes for 1982 and 1997 based on aid volumes are correlated at .87. An index based on all AiDA records (some dating to the late 1940s) turns out to be correlated with one based on activities with start dates of 1990 or later at .81, so these problems with the data may not matter much in practice.

Both fragmentation measures are equally subject to some criticisms. Most notably, neither donor expenditure shares nor project count shares necessarily accurately reflect the level of involvement and influence in a recipient’s development program. Neither measure distinguishes other characteristics of donors, only their “market shares.” One donor may undertake its activities in ways that are less intrusive and less institutionally corrosive than another donor with a similar share of aid.

Fragmentation can be computed for different aid sectors, such as education, health, and water, because projects (in AiDA) and aid commitments (but not disbursements, in the DAC data) are coded by sector. A high level of fragmentation overall is in principal consistent with donor specialization and hence low fragmentation in individual sectors. Mean levels of fragmentation in fact are somewhat—but not dramatically—lower within individual sectors. However, in countries where fragmentation overall is high, fragmentation within sectors also tends to be high.

The AiDA data base can also be used to construct a project proliferation measure from the total count of projects recorded in a period (with or without adjusting for size of the recipient country), subject to the limitations noted above. The number of projects with start dates of 1982 and later are highest for India, with 3,800, and Tanzania, with 2,758. Correlations of project counts with donor fragmentation are positive and significant but modest in strength, whether fragmentation is computed from disbursement shares (.39) or project count shares (.36).

Predictions of the model regarding the impact of project counts are ambiguous. In the fully nonrival case, the number of managers hired by donor d for project i, Mi, declines with N, the number of projects. However, if it declines less than proportionately with increases in N, N*Mi (the total number of managers hired to work on donor-funded projects) would increase with N. This is consistent with a common view within the aid

> 13 The number of projects with start dates for 1987 is five times the number for 1986, and the number doubled from 1994 to 1995, suggesting that some donors failed to report start dates until recently.

15
||||||maximisation and<br>increases the value of<br>regulation|
|---|---|---|---|---|---|
|Mota (2003)|single<br>country case<br>study|Brazil|privatisation of<br>electricity<br>distribution and<br>supply<br>businesses<br>between 1995-<br>2000|social cost-<br>benefit<br>analysis|economic welfare<br>(net benefits) was<br>significant but most<br>of it went to the<br>producers;<br>consumers could<br>have benefited more<br>from privatisation in<br>the presence of<br>tougher regulation|
|Anaya<br>(2010)|single<br>country case<br>study|Peru|privatisation of<br>electricity<br>distribution<br>companies:<br>Electrolima and<br>Electro Sur<br>Medio|social cost-<br>benefit<br>analysis|privatisation was<br>worthwhile in terms<br>of social welfare,<br>government and<br>producers benefited<br>the most while<br>consumers the least<br>due to price increases|
|Toba (2007)|single<br>country case<br>study|Philippines|liberalisation of<br>the electricity<br>generation sector<br>between 1990-<br>1993|social cost-<br>benefit<br>analysis|consumers and<br>investors were net<br>gainers while the<br>government lost,<br>reform with private<br>sector participation<br>increased economic<br>welfare|
|Tishler et al.<br>(2006)|single<br>country case<br>study|Israel|in accordance to<br>the 2003 Israeli<br>government<br>announcement to<br>undertake a<br>comprehensive<br>reform of the<br>electricity sector<br>based on the<br>standard reform<br>model|cost-benefit<br>analysis|the reform plan will<br>only yield a small net<br>benefit even when<br>carried out<br>flawlessly, will<br>increase the profit for<br>producers and tax<br>receipts for the<br>government at the<br>expense of<br>customers,<br>performance-based<br>regulation of the<br>sector thereby is<br>desirable|

33
- 42 -

possible productivity gains would serve to reduce perceived investment needs on a major scale. The same observation applies to warehousing, and -to a considerable extent- to the railways. The situation in the highway sector is substantially different because of the technical limitations of most roads, and shortage of special trucking equipment. Similarly, the communications sector is critically limited in its capacity tc accommodate the growing electronic networking requirements of trade and transport.

100. A consolidated assessment of all perceived investment needs in the trade logistics sector does not exist. Such need assessments are presently rather fragmentary; they are the outcome of deliberations by the various committees, established to study issues in different sectors of the national trade logistics system.

|Estimated Canital Investment Requirements until|the Year 2000|
|---|---|
|in different segments of the Indian Trade Logis|tics System|
|Sector<br>Invest<br>(US$,|ment Needs<sup>10 6</sup><br>billion)|
|Ocean Shipping|6.13|
|Coastal Shipping|1.82|
|Ports|2.50|
|Railways||
|Track and Facilities|4.60|
|Rolling Stock|2.20|
|Highways|6.40|
|Trucking Industry|1.50*|
|Aviation||
|Cargo Carriers|3.50*|
|Airports|2.05*|
|Warehousing (ICDs/CFSs)|0.75|
|Communication Networks|0.55*|
|Total|32.00|
|(Note: April 1989<br>costs,<br>inflated<br>to<br>cover<br>consideration.)|the period under|

101. The above estimates are based on the assumption that the modal shares in container transport will not change significantly, i.e. railways remain the principal mode for land transport. It is. however, by no means clear that such arrangement would yield the lowest overall investment package -and maximum service efficiency. Consequently, it has to be recognized and accepted that under these present circumstances, any investment to establish new or expanded cargo handling, warehousing or transport facilities is not certain to yield optimal benefits. Not on y

**106/ Essentially** to **cater for container trades. Estimates by d;fferent committees, supplemented by** Bank **estimates** (identified **by** **_an_ asterisk).**
suffer from. We estimate the production function separately for sectors and years with establishments’ real output as the output measure.<sup>20</sup> This approach allows us to extract the unbiased estimates of the establishment-level output elasticities required to determine an unbiased measure of establishments’ productivity (TFP) and markups that accounts for unobserved productivity shocks across establishments facing heterogeneous technologies and potentially imperfect competition. More specifically, the estimated output elasticities are computed using the estimated coefficients of the production function and the establishment-level information on labor, capital, and age—the output elasticity for labor under the translog production function, for instance, is given by Note that the use of the standard Cobb-Douglas production function implies that the output elasticity of labor is simply given by β<sup>�</sup> ��. We use the estimated unbiased output elasticities in (3) to compute establishments’ TFP and markups, whereby we compute the latter by multiplying the output elasticity to labor with the ratio of establishments’ sales over total labor costs.

# **5.2 Subnational regulatory reform impact**

The matched subnational regulatory reform and establishment census panel data allow us to estimate the effects of eliminating subnational barriers to market entry on establishments’ productivity in a difference-in-differences setting. The census data are available from 2008-15 so that we can track productivity and markups of establishments impacted by one of the subnational reforms for up to 5 years before and 2 years after the changes in the national legislation expanding the enforcement power of Peru’s competition authority over subnational jurisdictions. We thus consider the following generalized difference-in-differences specification: where _i_ stands for an establishment, _d_ for an establishment’s state ( _departamento_ ) or province municipality, _m_ for an establishment’s municipality (district or province), and _t_ for the year. Y���� measures TFP or markups as defined earlier. R��� is the regulatory reform indicator; it is a dummy variable which is equal to 1 in the years of and after a reform for establishments located in the

> 20 We distinguish between manufacturing, commercial, and other services to have at least 1,000 establishments per year, needed to ensure a robust TFP estimation based on the Ackerberg approach.

16
|Rural Non-Poor|17.7%|18.5%|32.7%|
|---|---|---|---|
|Total|100.0%|100.0%|100.0%|
|Poor|19.6%|42.8%|19.4%|
|Non-Poor|80.4%|57.2%|80.6%|
|Urban|67.9%|50.8%|54.1%|
|Rural|32.1%|49.2%|45.9%|

_Source:_ Panama Semi-Input-Output model simulations.

Because of data uncertainties and simplifying assumptions used, the above analysis of the effects of increases in agricultural output on incomes in the Panama economy illustrates only the broad order of magnitude of the effects. Nonetheless, the broad structure of production, and the structure of household incomes and demand are reflected in the analysis.

It is important to note, however, that the simulations imply an even distribution of the gains to returns to labor and capital across all owners of these factors. This assumption is valid if labor markets function well so that increases in labor demand are reflected in general rises in wage rates that benefit all workers. Yet, location of activities matters a lot for local labor markets and especially for returns to capital and backward linkages to agriculture. The implication is that including much of the rural poor (and minorities) in the benefits of growth in tourism will require that these households are well integrated in product and factor markets where the investments take place.

Some of the assumptions underlying this model cannot be verified without destination level data. For example, it is necessary to ascertain such underlying basic facts as whether certain household groups tend to be precluded from employment in all-inclusive resorts; whether local agricultural products are deemed as suitable for purchase by hotels and restaurants; or whether any obstacles exist to development of handicrafts and local services as hotels are built. To answer these kinds of questions and develop monitoring of local-level impacts, it is necessary to conduct destination level surveys of at least a few selected areas where tourism has already developed.

# **7. Conclusions and Policy Recommendations**

The tourism sector has large significance for the Panamanian economy because of very high and rapidly increasing arrivals of foreign tourists and high levels of spending. This is evident from very large contribution of the sector to GDP—somewhere in the range of 6 to 9 percent; arrivals of foreign tourists that exceed 70 percent of the country’s population over a year; and particularly high economic significance in relative terms in peripheral areas such as Bocas del Toro. Furthermore, the tourism sector has the highest multiplier effects on the economy—that are nearly double the multiplier of Zone Colon and the Canal—because of very high backward and forward linkages. The sector also has a large potential to benefit the poor—most of the gains in household incomes from Zone Colon, the Canal and other sectors of the economy accrue to urban non-poor households 16
18

## **Table 3. Credit-less Recoveries and IMF-Supported Programs**

||<br>[1]<br>IMF<br>Program|<br>[2]<br>Economic<br>Conditions 1|<br>[3]<br>Economic<br>Conditions 2|[4]<br>Economic<br>Policies|
|---|---|---|---|---|
|**Output Shock**|||||
|GDP Growth|-0.100|-0.120|-0.120|-0.173|
|(change; trough minus peak)|[0.023]***|[0.027]***|[0.030]***|[0.039]***|
|**External Adjustment**|||||
|Real Effective Exchange Rate|-0.008|-0.005|-0.002|-0.006|
|(% change; trough to peak)|[0.013]|[0.014]|[0.015]|[0.017]|
|Current Account Balance|0.057|0.059|0.070|0.120|
|(% of GDP; change; trough minus peak)|[0.032]*|[0.034]*|[0.038]*|[0.046]***|
|**Openness Characteristics at Peak**|||||
|Exports of Goods and Services|-0.021|-0.022|-0.022|-0.037|
|(% of GDP; peak)|[0.009]**|[0.010]**|[0.008]***|[0.010]***|
|Capital Account Openness|0.288|0.272|0.401|0.547|
|(index: higher, more open; peak)|[0.123]**|[0.134]**|[0.149]***|[0.192]***|
|**IMF Program at Trough**|||||
|Use of IMF Credit (GRA)|-0.639|-0.901|-1.354|-2.178|
|(dummy variable; trough)|[0.548]|[0.533]*|[0.596]**|[0.680]***|
|**Economic Conditions at Trough**|||||
|Public Debt||0.013|0.011|0.016|
|(% of GDP; trough)||[0.006]**|[0.006]*|[0.007]**|
|International Reserves||0.001||0.019|
|(% of GDP; trough)||[0.018]||[0.018]|
|Inflation|||0.052|0.121|
|(year-on-year % change; trough)|||[0.025]**|[0.038]***|
|**Macroeconomic Policies**|||||
|Fiscal Policy Easing||||0.201|
|(change; trough minus peak)||||[0.069]***|
|Monetary Policy Easing||||-0.022|
|(change; trough minus peak)||||[0.006]***|
|Constant|-1.148|-2.007|-2.266|-3.290|
||[0.381]***|[0.571]***|[0.650]***|[0.729]***|
|Number of Episodes|93|93|93|93|
|Number of Countries|52|52|52|52|
|Pseudo R2|0.24|0.28|0.32|0.47|
|Log Pseudolikelihood|-42.37|-40.20|-38.29|-29.53|

Note: Robust standard errors are in brackets. *, ** and *** denote statistical significance at the 10 percent, 5 percent and 1 percent levels, respectively. Source: Authors’ estimations.

# **VI. Concluding Remarks**

Credit-less recoveries are neither rare nor insurmountable challenges. The empirical evidence suggests that such recoveries occur at a much slower pace and are only somewhat more common among emerging markets. But recoveries do eventually occur. In fact, economic performance is in large measure correlated with the depth of the correction triggered during the economic adjustment that precedes the trough; specifically, the size of the downturn and the extent of external adjustment that typically accompany a recession (from the current account adjustment to developments in exchange rates). Also, openness has a dual role. Trade openness decreases the likelihood of a credit-less recovery as trade is a more stable source of financing. Conversely,
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http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/ECAEXT/RUSSIANFEDERATI ONEXTN/0,,contentMDK:20919706~menuPK:2560592~pagePK:1497618~piPK:217854~theSit ePK:305600,00.html 33