rafmacalaba/datause-encoder-data huggingface dataset:
# **Restructuring** of **Insider-Dominated Firms:** A Comparative **Analysis**

# **1. Introduction**

A large number of recent studies suggest that privatization and the ensuing change in corporate governance are essential conditions for enterprise restructuring in transition economies. State enterprises are generally found to be less efficient, have excess labor and higher wages, and tend to accumulate losses (Frydman et al, 1997, Pohl et al, 1997). Relatedly, privatization has been found to lead to significant improvements in firm performance (Megginson et al., 1994; Hare and Peev, 1995).

There is less agreement on whether particular methods of privatization and modalities within these methods yield more restructuring. A detailed survey of case study evidence (Carlin et al, 1995) shows, for a sample of Czech, Hungarian, Russian, and Bulgarian enterprises, that firrns owned by foreign investors perforr best, but there is little difference between local insider (manager and employees) and outsider dominated firms. Frydman et al. (1997), using a large data set of Czech, Hungarian, and Polish enterprises, find that private firms outperform stateowned firms, but that outsider-dominated firrns do not outperform insider-dominated firms. Smith et al. (1997) find that foreign dominated enterprises in Slovenia have the highest growth in value-added, while insider-controlled firms have a higher average growth when compared to firms controlled by outside local investors. Earle and Estrin (1997) and Linz and Krueger (1998) find no evidence of a robust relation between methods of privatization and enterprise restr-ucturing in Russia, using several alternative specifications. Similar conclusions are reached in Estrin and Rosevear (1 998) for Ukrainian enterprises.

In this paper we extend the literature by investigating the effect of different modalities of privatization to insiders on the restructuring process in two forner Soviet republics-Georgia and Moldova--using enterprise survey data for 1995-97. Enterprise restructuring was similar in companies where incumbent managers received significant ownership stakes for free and in still state-owned companies. In contrast, the restructuring process was faster in companies bought by
independent relationship between these bank efficiency measures and bank regulations, we control for an array of bank-specific traits, including the bank’s market share, its size, the liquidity of its assets, bank equity, and the proportion of income that the bank receives in noninterest bearing assets.

The results shown in Table 1, equations (4) and (5), again advertise the benefits of regulations that empower private-sector monitoring of banks. These regressions use a crosssection of 1,362 banks across 68 countries. The bank-level data are averaged over the period 1995-1999. We average over a few years to reduce the potential impact of business-cycle fluctuations on these measures of bank efficiency, but obtain similar results hold when using data from 1999 only. Private Monitoring is associated with greater bank efficiency, as measured by lower levels of Net Interest Margin and Overhead Costs. These findings, and those in DemirgucKunt, Laeven, and Levine (2004), suggest bank regulatory and supervisory policies that foster private-sector monitoring enhance bank efficiency.

IV. C. Simulation mechanics The simulation mechanics for the bank development and efficiency regressions are straightforward. These are simple linear regressions from the estimated relationships in Table 1: where Y is either bank development, the net interest margin, or overhead costs, X is the matrix of explanatory variables from Survey I listed in Table 1 for each regression, α and β are the estimated parameters shown in Table 1.

Differencing the above equation yields where ∆X is the change in the explanatory variables between Survey I and Survey III. Specifically, it is the value in Survey III minus the value in Survey I. This equation then provides the forecasted or simulated change in Y (bank development, the net interest margin, or overhead costs) resulting from reforms to the regulatory system between Survey I and Survey III, based on the estimated relationships from Survey I reported in Table 1. We assume that the nonregulatory variables remain fixed and therefore only focus on estimating the effects of the change in regulatory policies on the banking system. We performed the simulations of regulatory 13
A closer look at the data reveals some interesting variations when the countries in the sample are grouped regionally.

# <u>East Asia</u>

<!-- Start of picture text --> Figure 2. Perceptions of Judicial Figure 3. Formalism Index<br>Efficiency 3.70<br>10.0 9.2 9.0 3.60 3.52<br>9.0 8.3 7.7 3.50<br>8.0 7.3 3.40<br>7.0 5.8 6.3 3.30<br>6.0<br>3.20 3.13<br>5.0<br>4.0 3.1 3.10 3.00<br>3.0 3.00 2.91<br>2.0 2.90<br>1.0 2.80<br>0.0 2.70<br>Judicial Citizens' Access Enforcement Control of Check Collection Eviction<br>Efficiency Index to Justice Index Corruption Index<br>High Income Countries East Asian Countries High Income Countries East Asian Countries<br><!-- End of picture text --> _Fig.1 Sources: Judicial Efficiency Index: ICRG; Citizens’ Access to Justice: World Business Environment Survey; Enforcement Index: Business Environmental Risk Intelligence; Control of Corruption: ICRG. Fig.2 Source: Djankov et al. (2003)_ The East Asian countries in the sample score lower on various performance indicators and on average have more procedural complexity than high income countries (statistically significant at the 5% level). Figure 3 shows an index measuring overall procedural complexity for both the check collection and eviction cases.

A number of procedures seem to matter particularly: for example whether or not professionals are required to represent the litigants in court, whether the procedure relies on judges who have undergone complete training or not<sup>33</sup> , whether or not there is a specialized court (such as a small claims court). Other aspects which are important are the nature of the legal justification required (how formal a justification is required), the number of independent procedural actions required (that is how many actual steps are needed in the filing and service of a complaint, trial and judgement and enforcement), and how the presentation of evidence is regulated (e.g. only certified documents are

> 33 Dispute resolution may also be conducted by an arbitrator, or an administrative officer, practicing merchant etc.

30
argument by specifically controlling for household 1990 asset position in our outcome regressions, so that the only impact exerted by the instrument on household self-employment is through its effect on past migration.<sup>11</sup> Network variables are widely used in the literature to instrument for migration. We employ _Network90_ as an instrument to capture the presence of household migration networks that existed in 1990, and that might have had influenced the past migration decision, fulfilling the condition of instrumental relevance. The variable refers to non-household members; thus it is less likely to bear any relation to unobserved characteristics that would be predictive of their non-farm business involvement. Even if one may claim that pre-1990 international migrant networks are likely to supply households with more connections and possibly increase their exposure to non-farm activities, it should be noted that _Network90_ refers to 15 years back in time relative to the present state of household business ownership, thus making the possible link even more tenuous. With these arguments in mind, we assert that the variable would not necessarily influence a household’s likelihood to engage in non-farm self-employment in 2005, meeting the requirement of instrumental exogeneity.

In the second model, we utilize _Satellite90_ as a possible instrument, assuming that the ownership of a satellite dish before the legalization of international migration might have allowed Albanian households to gain access to Italian TV channels, and affected their attitudes towards migration. In this respect, the variable is likely to possess instrumental relevance. To claim instrumental exogeneity for _Satellite90,_ we rely on the time-dimension argument provided in defense of the exogeneity of _Network90_ . In addition, as satellite ownership may be indicative of differences in initial wealth, and thus suggestive of involvement in non-farm business activities. For this reason, controlling for 1990 household asset position in our regressions also strengthens the claim concerning the exogeneity of _Satellite90_ .

Furthermore, _AverageShocks_ is another instrument candidate, computed for migrant households<sup>12</sup> as the average annual number of household-level shocks until their first migration episode. The variable refers to a series of events that might have triggered a household’s decision

> 11 To control for household asset position in our regressions, we constructed a household wealth index via principal components analysis. The index accounts for the number of following items that may have been owned by households in 1990: a color TV, a black and white TV, a tape/cd player, a refrigerator, a washing machine, a sewing/knitting machine, and a bicycle.

> 12 For non-migrant household, the variable is computed as the average annual number of shocks between 1990 and 2005.

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share of imports in GDP times the growth rate of the import deflator. All data for these variables is taken from the World Bank’s World Development Indicators database.

Table 3 reports the pairwise correlations among our determinants of debt distress. Not surprisingly, the present value of debt relative to exports, and debt service relative to exports, are quite strongly correlated at 0.45 in our sample. All of the remaining pairwise correlations are quite small, and with the exception of the correlation between policy and the present value of debt at –0.26, are less than 0.20 in absolute value. This gives us some indication that there is sufficient independent variation across our different determinants of debt distress that we can identify the partial effects of most of them.

Figure 3 illustrates the simple bivariate relationship between each of our explanatory variables and the distress indicator variable. In each panel, we divide the sample of observations by deciles of the explanatory variable of interest. We then compute the mean value of the explanatory variable by deciles, and plot it against the mean of the distress indicator variable by decile. Thus, for example, in the first panel of Figure 3, the mean value of the present value of debt to exports in the top decile of this variable is just above 4, and the proportion of distress observations in this decile is just above one-half.

The first part of Figure 3 shows that there is a strong positive correlation between a variety of debt burden indicators and the incidence of debt distress. An interesting feature of these graphs, however, is that the maximum value on the vertical axis rarely exceeds 0.5. This means that, even when traditional debt indicators are extremely high (in the upper deciles), there are many cases where debt distress does not occur. For example, in the case of the present value of debt relative to exports, only nine of 17 episodes in the top decile of this variable correspond to distress. Among the non-distress episodes in the top decile are Bangladesh, Burundi and Morocco, which all had a present value of debt of more than three times exports for most of the late 1980s and early 1990s yet did not experience debt distress. It is also interesting to note that there is little 14
# **Assessing the Effectiveness of Social Protection Measures in Mitigating COVID-19-Related Income Shocks in the European Union**<sup>**1**</sup>

Katrin Gasior<sup>a,b</sup> a ISER, University of Essex c International Inequalities Institute, London School of Economic and Political Science

**JEL:** D31, E24, H24, I38

**KEYWORDS** : Social protection, Social Safety Nets and Transfers, Social Welfare, COVID-19, Poverty and Fiscal, Financial and Economic Crisis, EU, Income Distribution.

> 1   The results presented in this paper are based on EUROMOD version I4.0+. Originally maintained, developed and managed by the Institute for Social and Economic Research (ISER), since 2021 EUROMOD is maintained, developed and managed by the Joint Research Centre (JRC) of the European Commission, in collaboration with EUROSTAT and national teams from the EU countries. We are indebted to the many people who have contributed to the development of EUROMOD. The results and their interpretation are the authors’ responsibility. This paper is a deliverable of the RSR-18 funded project “Analytic Tools to Assess Social Protection Systems’ Responses to COVID19 and their Sustainability in ECA (P176852)”.
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The C8te d'Ivoire is characterized by a sharp earnings differential between urban and rural areas: average base earnings from the main job in Abidjan are 4 times those in rural areas, average total earnings in Abidjan are 4.5 times those in rural areas (Table 14). The differential is most pronounced for the self-employed and smallest for informal sector employees. Earnings in Abidjan are also higher than those in other cities, except for employees in the formal private sector.<sup>13</sup> / Government employees command the highest salaries, except in other cities where employees in the formal private sector are at the top.

The payment of bonus/commissions and wages-in-kind is largely limited to the modern sector in urban areas. These payments make up about _20%_ of base wages, except 'or private sector employees in other cities for whom they constitute a hefty 37% of base wages. The reason for this high premium, as well as for the fact that private sector employees in other cities earn a higher base salary than their colleagues in Abidjan, is likely to be the enormous pull factor emanating from Abidjan. Without a higher remuneration and substantial bonuses and in-kind benefits, modern firms might find it very difficult to attract workers for their establishments outside Abidjan. In general, the labor market in other cities is much tighter than in Abidjan. The overall unemployment rate is only 3% as opposed to 19% in Abidjan.

- 13/ The distinction between the formal and informal sector was based on a series of questions in the CILSS specifically designed to elicit the degree of legal and institutional protection accorded to workers-with the absence of such protection being characteristic of the informal sector. The questions pertained to the existence of a written employment contract between worker and entexprise, the application of minimum wage laws, the existence of a union in the firm, and the receipt of various social security benefits (paid leave, paid sick leave, retirement fund, etc.).
# **Tables**

Table 1: Descriptive statistics of our study samples: riders through the app and platform survey respondents

||Riders<br>Pl<br>Mean/SE<br>(1)|atform survey: women<br>Mean/SE<br>(2)|Platform survey: men<br>Mean/SE<br>(3)|Diference<br>(2) - (1)<br>(4)|Diference<br>(2) - (3)<br>(5)|
|---|---|---|---|---|---|
||_Panel A: Demo_|_graphic variables_||||
|Employed|0.678|0.913|0.927|0.235***|-0.0150|
||(0.025)|(0.018)|(0.017)|||
|Age|32.51<br>|37.22<br>|36.41<br>|4.712***|0.808|
||(0.617)|(0.665)|(0.763)|||
|Years of schooling|12.95|11.31|11.01|-1.643***|0.304|
||(0.186)|(0.277)|(0.241)|||
|Number of rides in a typical week|6.055|8.467|8.934|2.412***|-0.467*|
||(0.260)|(0.169)|(0.183)|||
|Crime rate at rider home station|1367|1233|1178|-134.073**|54.85|
||(38.908)|(48.298)|(42.969)|||
|Violent crimes at rider home station|45.39|47.77|46.91|2.377**|0.853|
||(0.660)|(0.785)|(0.717)|||
|Crimes against property at rider home station|263.8|228.9|214.8|-34.901**|14.08|
||(9.427)|(11.796)|(10.558)|||
|_Panel B: Self-reporte_|_d risk of harass_|_ment (number of occurr_|_ences in a year)_|||
|Physical, reserved space|12.35|25.86|28.50|13.502***|-2.639|
||(2.578)|(4.035)|(4.419)|||
|Physical, public space|21.14|62.75|64.13|41.615***|-1.382|
||(3.555)|(5.026)|(5.317)|||
|Verbal, reserved space|22.95|39.80|39.64|16.849***|0.165|
||(3.644)|(4.622)|(4.906)|||
|Verbal, public space|40.08|84.47|82.97|44.393***|1.5|
||(4.682)|(5.335)|(5.606)|||
|_Panel C: Self-reported _<br>|_share of reserved_<br>|_space rides under hypo_<br>|_thetical scenarios_|||
|Status quo|0.472|0.551|-|0.079***|-|
||(0.017)|(0.022)|-|||
|Current scenario, 30 cents opportunity cost|0.374|0.466|-|0.092**|-|
||(0.023)|(0.040)|-|||
|Current scenario, 65 cents opportunity cost|0.278|0.374|-|0.096**|-|
||(0.021)|(0.040)|-|||
|No men on reserved space, 30 cents opportunity cost|0.514|0.577|-|0.0630|-|
||(0.025)|(0.040)|-|||
|No men on reserved space, 65 cents opportunity cost|0.385|0.511|-|0.125***|-|
||(0.023)|(0.042)|-|||

_Note:_ Unit of observation is one rider in column (1) and one platform survey respondent in columns (2) and (3). Sampling weights are applied to platform survey observations. Standard errors in parentheses, clustered at rider level. For risk of harassment questions, women were asked to imagine a rider similar to themselves who takes the same commuting route, while men were asked to imagine an average woman who takes the same commuting route. Crime data from Instituto de Seguranca Publica do Rio de Janeiro is reported as number of crimes per 100k residents in the area.
# Expected impact on Mexican industrial exports values from improvements in Trade Facilitation

|Source|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
|ifexico's unilateral improvement|||||||||||
|Port Efficiency "i"|13.6|9.5|0.0|0.0|2.1|20.2|65|19.7|5.1|7.6|
|Corruption “i|12.6|6.9|0.7|11.5|17|16.3|0.0|0.0|34.5|51.4|
|Custom Environment "1"|3.0|2.1|0.0|0.0|0.5|43|3.9|14.1|0.0|0.0|
|Service Sector Infrastructure "\"|2.6|1.8|0.0|0.0|0.0|0.0|-1.5|5.3|0.0|0.0|
|Total Exporter|31.8|22.4|0.7|11.5|4.4|41.2|7.9|28.4|39.7|59.0|
|improvements in ifexican partner|s||||||||||
|Port Efficiency ")"|33|23|0.3|53|0.1|1.3|0.4|1.5|1.0|1.5|
|Corruption "]"|0.0|0.0|0.1|1.4|0.0|0.0|-0.4|-1.5|0.0|0.0|
|Custom Environment ")"|-2.1|“1.5|-0.1|-1.6|0.0|0.0|0.0|0.0|-0.5|-0.8|
|Service Sector Infrastructure "j"|0.8|0.6|-0.4|“1.4|0.1|0.6|0.4|1.3|0.6|0.9|
|Total Importer|2.0|1.4|0.2|4.2|0.2|1.9|1.1|1.35|1.1|1.6|
|Totalimpact<br>|Ee)|<0|)|||A)|6)|2)|0|Cc)|0|
|<br><br>Source:Ownestimates|||||||||||
dominate the vanilla intermediation business. Taken at face value, this suggests that there was little entry since the mid-1990s or that, if there was, new entrants followed the incumbents' pricing policy.<sup>23</sup> Under the counterfactual, producer prices are reduced very sharply compared to their current values. In percent of the current FOB price, the simulated producer price lies between 2% and 11%, as against 22% currently. The after-tax FOB price, by contrast, shoots up by a factor ranging between 2.3 and 4.4. This price increase is obtained by a sharp contraction of volumes exported, which go down by a factor between 3 and 6. In accordance with intuition, Indonesian prices and volumes both go up (although by much less) as Indonesia free-rides on Madagascar’s output restrictions. These simulations, suggesting drastic effects on prices and quantities, no doubt provide upper bounds on the effect of change in the policy environment induced by the 1995 reforms.

Once re-introduced in the household survey, however, the new producer prices turn out to have a meek effect on the distribution of income, as shown by the last columns of Table 7 and Figure 7. The reason is essentially that most of the effective consumption of Malagasy rural households is self-produced. Cash income represents a small part of their effective income, and vanilla is only part of it. Thus, even drastic price changes do not change the picture much in terms of poverty. Using a central scenario with a unit elasticity of supply, a unit elasticity of vanilla demand and an elasticity of substitution between Malagasy and Indonesian varieties equal to two, the change in the poverty headcount ratio suggests that only about 20’000 individuals were lifted out of poverty as a result of the 1995 reforms.

# **4. Concluding Remarks**

The reforms initiated in 1995 at the behest of international donors (elimination of the marketing board and phasing out of export taxes) put to an end two decades of a policy of over-exploitation of market power on the outside front and extortion of farmer incomes on the domestic one that was clearly unsustainable.

> 23 Our counterfactual experiment reduces the number of traders to one, based on the assumption that they colluded during the socialist era. With export quotas, they obviously had little incentive to compete on prices. Indeed, Blarel and Dolinky (1995) describe a process through which the political allocation of export licences increasingly concentrated power in the hands of a single exporter allowed to ship fixed quantities of vanilla alongside the State marketing board.

30
# **Appendix Table A.3: Causal analysis: estimates from MB-NIPW and Lewbel estimators** **<u>(junior middle school as the threshold for parental education)</u>**

|||**(2)**|**(3)**|**(4)**|
|---|---|---|---|---|
|||**Parents**<br>|**education and Oc**<br>|**cupation**<br>|
||**Estimates**|**Non-farm, low**<br>**edu**|**Farm, high**<br>**edu**|**Non-farm, high**<br>**edu**|
|**Impact of f**<br>**schooling**|**amily background on children's**<br>||||
|**_Daughters_**|||||
|**MB-**|Coefficient|**0.63**|**1.24**|**1.87**|
|**NIPW**|90% confidence interval|(0.18,1.02)|(0.75,1.49)|(1.47,2.24)|
||Coefficient|**0.55**|**0.80**|**1.71**|
|**Lewbel**|t statistics|2.72**|2.95***|7.07***|
||Breusch-Pagan heteroskedasticity test|191.01|193.45|201.51|
||Kleibergen-Paap rk Wald F statistic|433.12|285.63|278.42|
|**_Sons_**|||||
|**MB-**|coefficient|**1.19**|**1.61**|**2.23**|
|**NIPW**|90% confidence interval|(0.62,1.57)|(1.26,1.94)|(1.26,1.94)|
||coefficient|**0.89**|**1.24**|**1.99**|
|**Lbl**|t statistics|3.08***|5.29***|9.85***|
|**ewe**|Breusch-Pagan heteroskedasticity test|160.56|239.08|296.16|
||Kleibergen-Paap rk Wald F statistic|150.39|531.69|404.56|

**_Notes:_**

**_1._** BM-NIPW=minimum-biased normalized inversed probability weighted estimator;

**_2._** The computation of MB-NIPW uses STATA program written by Millimet and Tachernis (2010);

**_3._** The MB-NIPW estimator uses boostrapping of 250 replications for computing the confidence interval.

**_4._** ***, **, and * Denotes statistical significance at 1%, 5%, and 10% level.
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# TABLE 19: Comparing the Efficlency of Ejidos and Private Farms: (i) A Survey of La Laguna, 1967.

|1967 Pesos|Private<br>Large|farms<br>Small|individual<br>Ejldatarlos|Col<br>Good|lective eji<br>Average|dos<br>Bad|
|---|---|---|---|---|---|---|
|Gross product<br>per hectare|7,804|7,977|4,620|8.540|5.100|3,450|
|Gross product per<br>unit of capital|0.81|1.31|2.51|1.03|0.80|0.95|
|Gross product<br>per manday|129|47|43|58|48|31|
|Total factor<br>productivity|1.15|1.29|1.24|1.35|0.90|0.65|

# NOTES:

(I) N-208 farms; (2) 'Large farms' refers to those over ten hectares; 'small farms' have under ten hectares; (3) 'Good', 'average', 'bad' refers to the Ejido Bank's assessment of the creditworthiness of the collective ejidos In the sample; (4) Figures for private farms and individual ejldatarlos refer to Individual farm operations; figures for the collective ejidos refer to values for the whole ejldo divided by the number of ejldatarlos; (5) 'Gross product' refers to the value of all farm production In 1967: crops, livestock, forestry; Includes changes in inventory; (6) 'Capital' refers to the value of livestock, equipment and plantations, excluding land, but Including half the value of purchased Inputs (land and equipment rent, hired labour, irrigation water, fertilizer, pesticide, Interest on loans) as an estimate of working capital; (7) 'Mandays' excludes hired labour; (8) 'Total factor productivity' Is gross product divided by purchased Inputs plus capital plus an knputed cost for owners' family labour.

SOURCE: Ecksteln et al (1978), p. 64.