rafmacalaba/datause-encoder-data huggingface dataset:
# 7. **Risk-taking and Bank Restructuring**

A major cause of the East Asian financial crisis was the weak regulatory and supervisory frameworks set by the East Asian governments and central banks. Although the East Asian governments have been too lax in enforcing their rules in the past, there are signs that they are making efforts to prevent such mismanagement in the future. The East Asian governments have already taken many actions to restructure the banking systems'°. In Indonesia, so far, bank restructuring has affected 146 of the 237 commercial banks - 50 commercial banks have been shut down, another 26 commercial banks have been taken over by the government restructuring agency IBRA, and another 54 commercial banks have become directly controlled by the government. In addition, four of the seven state banks are to merge and 12 commercial banks have been nationalised. In Korea, four of the 26 commercial banks have been nationalised, five commercial banks have been absorbed into other institutions, and 2 commercial banks have been sold to foreigners. In addition, 16 of the 30 merchant banks and 10 of 25 leasing companies have been shut down. Since the Malaysian government has opted for absorption, there has been no closure of any of 35 commercial banks or 39 finance companies. Thus far, two Malaysian commercial banks have been absorbed by other entities, and 16 Malaysian finance companies have been absorbed by parent banks. There are plans to merge the remaining 58 institutions into 6 groups. In the Philippines no banks have been closed down. The Philippine government and central bank encourage consolidation of banks instead. Two major banks have already merged. In Thailand, one of the 15 commercial banks has been closed down, two commercial banks have been nationalised, and two commercial banks have been absorbed by government entities. In addition, 55 of the 91 finance companies have been closed down, and 16 finance companies have been absorbed by other entities. Finally, Thai banking legislation has been changed so that it is easier for foreigners to buy into Thai financial institutions.

If our risk-taking measure correctly measures excessive risk-taking by banks we would expect that our measure of risk-taking is higher for banks that have been restructured than for banks that have not been restructured. We will classify a bank as being restructured if it was either suspended, re-capitalised, merged, taken over, nationalised or closed down after 1997. The 14 foreign-owned banks in Thailand are all branches of foreign banks, and can therefore be supported by the parent if necessary. As a consequence, none of these banks have been restructured. We therefore exclude them from this ex-post analysis in order to prevent distortion '° Source: Goldman Sachs Investment Research; Claessens et al. (1999b). All figures as of Sept. 1999.

21
database. Of these, 635 (55%) were registered on both sides of the border, 330 (29%) by the Algerian authorities only, and 176 (16%) by the Tunisian authorities only. The average annual value of these trade flows was estimated at US$272m by the Algerian authorities and US$295m by the Tunisian authorities.

**The high value of these trade flows is due primarily to the dominance of trade in hydrocarbons (gas and petroleum), which accounted on average for 70% of Algeria's exports to Tunisia according to the Algerian authorities and 76% of Tunisian imports according to the Tunisian authorities.** This trade accounts for most of the gap in the total value of trade recorded by the two countries (or US$28.4m higher according to the Tunisian authorities).<sup>16</sup>

**The Tunisian authorities recorded data for imports of 68 different categories of products on average, while their Algerian counterparts recorded export data for 82 categories.** Although there are gaps in the data (with 13 fewer categories on average per year), these are not as large as with Libya.

**However, there are significant gaps in terms of both value and volume.** Table 2 (see Annex 2) lists the 20 largest recorded gaps for a single product. In the most extreme case (fresh fruit and vegetables), the export value was 30,000 times higher when recorded as exports by the Algerian authorities than when recorded as imports by the Tunisian authorities. In general, trade flows from Algeria to Tunisia are under-reported by the Tunisian authorities, with CIF figures lower than FOB in 55% of the cases. For the most part, gaps are found in the trade in products used mainly in the construction industry, such as categories 723 (civil engineering and contractors' plant and equipment), 747 (taps, cocks, valves, and similar appliances), 812 (plumbing and heating fixtures and fittings), and 273 (stone, sand, and gravel).

**In some sectors, the gap is significant across a number of years.** For example, this is the case for agriculture, with categories 012 (other meat and edible meat offals), 034 (fish), 036 (crustaceans, mollusks, and aquatic invertebrates), and 054 (vegetables). This is also true of groups of products covering measuring equipment and metallic tools (group 699).

**However, no trade gaps are recorded for some sectors where there is a strong likelihood of informal trade.** This is the case, for example, for petroleum and telecommunications equipment.

**Following the above example of Tunisia-Libya trade, if the most extreme trade gap figures are ignored (accounting for 32% of declarations made by the two countries), the average trade gap is 27% of the value declared by the importer.** However, if the most extreme figures are included, the average gap is 272 times the import value.

> 16 As noted in previous studies, (Berger and Nitsch 2012), it is very common to see disparities in the data concerning imports and exports of petroleum and petroleum products, principally because of fluctuations in their price on the international market.

9
Table 3: **Between-group differences in current salaries**

|||Part I men r|eference|Pooled param|eter reference|
|---|---|---|---|---|---|
||Total<br>Differencea|Characteristics<br>b|Structure|Characteristics'|Structure'|
|M1-M2|_5.0_|1.06<br>(.688)|3.93<br>(.883)|2.54<br>(.401)|2.45<br>(.401)|
|MI-Fl|14.23|8.64<br>(.439)|5.59<br>(.873)|9.23<br>(.285)|4.99<br>.644|
|MI-F2|19.33|8.75<br>(.722)|10.58<br>(1.16)|11.87<br>(.481)|7.46<br>(.684)|
|M2-FI|9.23|7.57<br>(.851)|1.66<br>(1.10)|6.69<br>(5.06)|2.54<br>(.428)|
|M2-F2|14.33|7.68<br>(.407)|6.65<br>(1.14)|9.32<br>(.315)|5.01<br>(.838)|
|F1-F2|5.10|0.11<br>(.745)|4.99<br>(1.76)|2.63<br>(.481)|2.47<br>(.690)|

Notes: a - Percentage difference in mean annual salaries between indicated groups.

b - Differences in mean salary attributable to differences in included characteristics.

- c - Differences in mean salary attributable to structure.

- b and c may not add up to a due to rounding off. Standard errors are given in parentheses.

# Table 4: Between-group **differences in entry level salaries**

|||Part I men r|eference|Pooled paramete|r reference|
|---|---|---|---|---|---|
||Total<br>Difference|Characteristics|Structure|Characteristics|Structure|
|M1-M2|9.21|2.35|6.86|5.24|3.97|
|MI-Fl|17.33|8.66|8.66|9.67|7.66|
|M1-F2|24.64|10.04|14.61|14.81|9.83|
|M2-F1|8.12|6.32|1.80|4.43|3.69|
|M2-F2|15.43|7.69|7.74|9.57|5.86|
|FI-F2|7.32|1.37|5.94|_5.15_|2.17|

38
in real world applications to conduct design-based validations. The results are expected to provide a guideline to others undertaking small area estimation. The results from design-based validation show that all small area estimation methods represent an improvement, in terms of MSE, over direct estimates. However, given the inherent high level of bias of methods relying solely on aggregate information (see Nguyen, 2012; Lange et al., 2018; Masaki et al., 2020) these should be used with caution and only under certain circumstances.

The paper first presents an overview of the small area estimation methods considered, directing readers who are interested in a more in-depth description to the appropriate sources. Then the model-based simulation design is detailed and results of the simulation experiments are presented. This section is followed by a description of the 2015 Mexican Intra Censal Survey and how it is adapted to represent a census and how samples are drawn from the created census. Results from the design-based simulation experiments are then presented. Finally, conclusions and lessons learned are provided.

# **2 Unit-level models**

The nested error model used for small area estimation by ELL (2003) and Molina and Rao (2010) was originally proposed by Battese, Harter and Fuller (1988) to produce county-level corn and soybean crop area estimates for the U.S. state of Iowa. For the estimation of poverty and welfare, the ELL and MR methods assume the transformed welfare _ych_ for each household _h_ within each location _c_ in the population is linearly related to a 1 _× K_ vector of characteristics (or correlates) _xch_ for that household, according to the nested error model: where _ηc_ and _ech_ are respectively location and household-specific idiosyncratic errors, assumed to be independent from each other, following: where the variances _ση_<sup>2and</sup><sup>_σ_</sup> _e_<sup>2areunknown.Here,</sup><sup>_C_isthenumberoflocationsinwhichthe</sup> population is divided and _Nc_ is the number of households in location _c_ , for _c_ = 1 _, . . . , C_ . Finally, _β_ is the _K ×_ 1 vector of coefficients. Under the original ELL methodology, the locations indexed with _c_ are supposed to be the clusters, or primary sampling units (PSUs) of the sampling design and do not necessarily correspond to the level at which the estimates will be ultimately produced. In fact, clusters are typically nested within the areas of interest (e.g. census enumeration areas within large administrative areas). Presenting estimates at a higher aggregation level than the clusters (for which random effects are included in the model) may not be appropriate in cases of considerable between-area variability, and may underestimate the estimator’s standard errors (Das 3
## **5.2.4 Second round impact from treatments**

Similar to the online survey, we illustrate there were potentially “second round” effects from accurate information about the benefits of COVID-19 vaccines as treated respondents were more likely to encourage other household members to get vaccinated. As is the case above we combine treatments to maximize statistical power of this “downstream” measure of the impact of information. We show that the treatments had a non-trivial impact on the likelihood respondents would encourage a household member to get vaccinated. The overall positive treatment effect was driven entirely by respondents who were in households where other members were unwilling to get vaccinated (see Figure 14). In contrast, there was a negative treatment effect on respondents who were in households where other members were willing to get vaccinated (see Figure 14).

# **5.3 Summary of Findings from the SMS Message Experiment**

The SMS message experiment closely replicates the main findings from the online experiment. Firstly, consistent with Hypothesis 1 and the results of the in-person survey, there is a strong association between the attitudes towards COVID-19 vaccines among members of the same household. Secondly, consistent with Hypothesis 2, accurate information about the benefits of getting vaccinated improved attitudes towards vaccines and this did not vary greatly based on whether individual or household level benefits were emphasized. Thirdly, counter to Hypothesis 3, accurate information was far more effective at decreasing unwillingness to get vaccinated among those without other household members who were willing to get the COVID-19 vaccine. Finally, we present suggestive evidence that there may be second round effects from accurate information, whereby respondents who received a treatment were more likely to encourage their household members to get vaccinated (this was entirely driven by those without other household members who were already willing).

36
By using firm-level data together with a novel set of instrumental variables, we distinguish between supply-side and demand-side influences. We first estimate the countrylevel relation between capital inflows and equity issuance. We regress each country’s equity issuances on equity inflows, taking into account country and year fixed effects. We find a strong association between the two. For every million U.S. dollars foreign investors purchase of emerging market equity, the value of seasoned issuance proceeds increases by 160,000 U.S. dollars.

We then move to the micro-level analysis to better understand the mechanisms that drive these aggregate issuance patterns. We first regress firm-level issuance proceeds on equity capital inflows, controlling for firm and year fixed effects. We find only a weakly significant correlation between capital inflows and the value of issuance proceeds for the typical emerging market firm.

When we divide firms into groups according to size, we find a large and statistically significant effect of equity inflows on the issuances of large firms. We define large firms as those in the top decile of size, measured by market value of equity within a country in the previous year. This result is consistent with the well-known fact that international investors are particularly interested in investing in the shares of large, well-established emerging market firms. The higher responsiveness of large firms’ issuances to equity capital inflows remains even after controlling for country-year fixed effects, which allows us to control for all timevarying country shocks. Moreover, we find that the relation between equity capital inflows and equity issuance is monotonically increasing in firm size.

Given that an increase in issuance activity could reflect either an increase in foreign equity supply by investors or greater domestic equity demand by firms, we develop an instrumental variable approach that identifies supply-side influences. We identify supply 3
that skill premiums in Mexico following NAFTA declined in parts of the country more integrated with world markets (relative to more isolated parts of the country).

_Indian and Brazilian trade liberalizations_ : Topolova (2010) exploits the 1991 Indian trade liberalization to estimate the impact of import competition on poverty. The estimates provide evidence that rural districts in which sectors were more exposed to liberalization were concentrated experienced slower decline in poverty and lower consumption growth. The impact of liberalization was most pronounced among the least geographically mobile at the bottom of the income distribution, and in Indian states where inflexible labor laws impeded factor reallocation across sectors.

Most empirical studies on the labor market effects of trade liberalization typically emphasize shortor medium-run effects. Dix-Carneiro and Kovak (2017) use 25 years of administrative employment data from Brazil to study the dynamics of local labor market adjustment following the country’s trade liberalization in the early 1990s. The study exploits variation in the tariff declines across industries and variation in the industry composition of local employment across regions to measure changes in local labor demand induced by tariff liberalization. The results reveal that regions facing larger tariff cuts experienced prolonged declines in formal sector employment and earnings relative to other regions. The impact of tariff changes on regional earnings 20 years after liberalization was three times the effect after 10 years. Importantly, these rising impacts on regional earnings are inconsistent with conventional spatial equilibrium models, which predict declining effects due to spatial arbitrage. Dix-Carneiro and Kovak (2017) examine potential mechanisms underlying these impacts, and find support for a mechanism involving imperfect interregional labor mobility and dynamics in labor demand, driven by slow capital adjustment and agglomeration economies. This mechanism gradually amplifies the effects of liberalization, explaining the slow adjustment path of regional earnings and quantitatively accounting for the magnitude of the long-run effects.

It is important to note that most studies reviewed above focus on the impacts of stronger import competition -- induced either by import tariff liberalization or by the rise of China as a major manufacturer exporter. The main exception refers to the studies on Mexico in the context of 34
inputs due to reduced inventory and reduced labor inputs for mending defective fabric. These improvements were estimated to have increased profits per plant by about $325,000 per year. We estimate that this represented, on average, a doubling of profitability.

# **III. THE 2017 FOLLOW UP**

## **_III.A. The Follow-up Process_**

In January 2017, working with the same consulting firm with which we had worked in 20082011, we re-contacted the 17 textile firms from the original study. Fortunately, all 17 firms agreed to work with the research team again on a follow-up study. This 100% uptake was aided by a combination of three factors: (A) the positive impact of the intervention in the first wave on the firms’ management and performance; (B) the stability of the firms, which had maintained the same address and contact details, and (C) the engagement of the same three consulting company partners and project manager as the 2008-2011 intervention.<sup>3</sup> One complication is that one single-plant treatment firm was in the midst of closing down after the owner's death. Without any close male relatives to continue the business, the owner’s wife had decided to sell the business, which, given its location, meant the business would stop trading and the site would be converted into residential housing.<sup>4</sup> One weakness of this follow-up wave is that our budget allowed us only two months of the consultants' time, which was sufficient to collect management data for all production sites and a basic set of firm performance indicators (e.g. on employment and looms), but not to collect detailed weekly output data that would allow TFP estimation, because that would have required extracting data on a firm-by-firm basis from log-books and accounting software. Consequently, our analysis is confined to management practices and basic performance indicators like employment or looms/employee, along with an imputed measure of labor productivity.

This follow-up data collection corresponds to an average period of 9 years since the implementation phase of the consulting intervention started and 7 years since it ended. It 3 These personal contacts are very important in our context. In fact, we delayed the start of this project to ensure we could staff the project with the same senior consulting team as the 2008-2011 wave.

4 The firm was over 30 years old, and due to the expansion of Mumbai was now located in a residential area so the land was more valuable as housing than for production.

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- 27 -

# TABLE 12s PETROCHEMICALS: PRIVATE SECTOR PROFITABILITY RATIOS

||1982-83|1985-86|1987-88|
|---|---|---|---|
|Basic Petrochemicals:||||
|Gross Profit to Sales(Z)|6.7|14.7|13.0|
|Return on Capital(Z)|8.8|21.7|20.1|
|Plastics:||||
|Gross Profit to Sales(Z)|11.0|12.9|13.3|
|Return on Capital(Z)|13.9|20.3|22.7|
|All Industries:||||
|Gross Profit to Sales(Z)|10.5|10.5|9.6|
|Return on Capital(Z)|16.7|16.0|13.3|

Source: Financial Performance of Private Companies, ICICI.

53. Other sources confirm the high profitability of private petrochemical firms. For a different sample of 9 large companies in 1988/89, the gross profit to sales and return on capital ratios were 18% and 22.3% respectively, yielding a net profit to net worth ratio of 27.3%.

54. Effective Protection granted to the industry by the complex system of import quota management and price administration has been high, ranging mostly between 60% and 177%. Table 13 summarizes the available EPR estimates for petrochemicals. EPRs relating to IPCL are based on actual price data of 1987 (Pursell, 1988). The other EPRs are derived from appraisal reports of DFIs for their 1988/89 projects.
- Aghion, P. and J. Tirole. 1997. “Formal and real authority in organizations”. _Journal of Political Economy_ , 105(1), 1-29.

- Alonso, R., W. Dessein, and N. Matsouschek. 2008. “When Does Coordination Require Centralization?” _American Economic Review,_ 98(1), 145-179.

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- Bloom, N., R. Sadun and J. Van Reenen, 2012. “The organization of firms across countries”, _Quarterly Journal of Economics_ , 127(4), 1663-1705.

- Bloom, N., L. Garicano, R. Sadum and J. Van Reenen, 2009, “The distinct effects of Information Technology and Communication Technology on firm organization”, NBER working paper No. 14975.

- Bolton, P. and M. Dewatripont, 1994. “The firm as a communication network”, _Quarterly Journal of Economics_ , 109(4), 809-839.

- Bresnahan, T. F., E. Brynjolfsson, and L. M. Hitt. 2002. “Information Technology, Workplace Organization, and the Demand for Skilled Labor: Firm Level Evidence”, _Quarterly Journal of Economics_ , 117(1), 339-376.

- Coval, J. D., and T. J. Moskowitz, 1999. ‘‘Home Bias at Home: Local Equity Preference in Domestic Portfolios,’’ _Journal of Finance_ , 54, 2045–2074.

- Dessein, W. 2002. “Authority and Communication in Organizations”, _Review of Economic Studies_ , 69, 811-838.

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25
third of the sample having less than 10 employees. Results confirm that there can be important differences between firms above and below the 10 employee cutoff.

For studies with census data using a 10 employee cutoff, the smaller firms (i.e. just above 10) are more likely to exit (Roberts and Tybout (1996); Tybout (2000)). However, in a study of microenterprises, Liedholm and Mead (1999) look only at enterprises with less than 10 employees. They find that young firms are most likely to exit, but that exit rates rise again as firms approach the 10 employee mark. In the work here, having a single continuous size variable does show a negative coefficient on size. However, creating dummy variables for three size categories (<10; 10-49, >49) allows for tests of whether there is a non-linearity in the effect of size. The results show that there can be, particularly with respect to competition and some measures of property rights where it is the middle sized firms, those with 10-49 employees, that are hit hardest. Results by Aterido et al. (2009) also confirm that there are non-linearities by size in the impact of investment climate conditions on other dimensions of firm performance, such as growth.

# _Firm Ownership: State Owned Enterprises_

The decision to keep an SOE operating can be related to a number of non-market oriented factors. Their soft-budget constraint makes it possible to continue operations even if they lose money and are not efficient producers. As such, it could be that their overall rate of exit is lower. A second question is beyond a lower average rate of exit, whether they are relatively more sheltered from the conditions of operating in a weak investment climate. If the state ownership shielded the firm from demands for bribes or gave the firm preferential access to the court system, SOEs could be less affected by investment climate conditions; 13