rafmacalaba/datause-encoder-data huggingface dataset:
by 163 percent in fiscal 1990/91, and by 48 percent in 1995/96. Table 23 also provides rough estimates of the interest rate banks would have had to charee clients to maintain their current level of profitability and cover program costs. In fiscal 1990/91. banks would have had to charge an annual interest rate of approximately 781 percent. By fiscal 1995/96, this figure had declined to 277 percent. To fund the direct costs of the program and maintain profitability in fiscal 1995/96, banks would have had to charge an interest rate estimated at 159 percent.

# **Table 23 Estimate of Subsidy Dependency Indexa and** Required Interest Rates to Cover Program Costs **for PHBK Program**

||||**Fisca**|**l Ycar**|||
|---|---|---|---|---|---|---|
||**199(/91**|199 1/92|1 )992/93|1993/94|**1994/95**|11995/96|
|Low.er bound estimate for SDI with full<br>program costs (perccnt)|628|<br>515<br>_l|<br>574|<br>471|<br>298|1<br>158|
|Lower bound estimate for SDI with direct<br>costs onlv (percent)<br>___|<br>163|<br>145<br>_|<br>163<br>_|<br>140<br>__L|<br>98<br>|1<br>48|
|LowNer<br>bound estimated average current<br>intercst rate (percent)b|107|<br>107|<br>107|<br>107|<br>107|<br>107|
|Estimated average interest ratc rcquired<br>to fundfullprogram cost (percent)|781|<br>659|<br>723|<br>613<br>_|<br>427|<br>277|
|Estimated average interest rate required<br>to fund direct program cost (ercen2t)|282<br>|<br>263<br>__3|<br>282|<br>257<br>257|<br>212<br>_22|<br>159<br>15|

a] The percent increase in the interest rate that is required if the program were to operate without subsidies. b/ Annual average intercst rate calculated on declining balance basis including all fees and forced savings requirements. An estimated average of the rates commonly charged by rural banks and commercial banks to channeling groups.

|_Outreach Scope._<br>PHBK experienced<br>significant difficulties<br>from fiscal 1991/92 to<br>fiscal 1993/94. Since<br>1994 h h|**PHB**|**T**<br>**K Progra**<br>1991/92|**able 24**<br>**m Lendi**<br>**F**<br>**1992/93**|**ng Volum**<br>**iscal Year**<br> 1993/94|**e**<br> <br>1994/95|1995/96|
|---|---|---|---|---|---|---|
|owever, te<br>||<br>|<br>|<br>|<br>|<br>|
|program has grown<br>rapidlv. In real terms,|Nominal loan<br>volume (Rp. million|3.534<br>)|2.915|3,406|6,813|12.821|
|PHBKvolume|Growth in real loan|64|-24|7|84|73|
|declined by 24|volume (percent)||||||
|percent in fiscal<br>1992/93and then|Nominal loan<br>volume (US$)|1,757.384|1,399,328|1,593,58|9 3,075,107|5.526.422|
|, <br>grewaby<br>cnt in<br>grew by'.7percent in<br>|<br>Numberofgroup<br>loans issued<br>|539<br>|365|773<br>|1,396<br> <br>|2,931<br>|
|fiscal 1993/94; 84<br>percentin1994/95;<br>and an estimated 73|Growth in number of<br>group loans issued|<br>29<br>_|-32|112|<br>81|110|

percent in 1995/96 (Table 24). Total loan volume was Rp. 12.8 billion (US$5.5 million) in fiscal 60
The Lerner index captures market power by calculating the markup of prices above marginal costs. We use 181,830 bank-year observations for 21,988 banks in 124 countries to compute this index (Appendix III). The bank data are obtained from BankScope. BankScope is the limiting factor for the sample period. The Lerner index is a widely used measure of competition (e.g., Koetter, Kolari, and Spierdijk (2012)). By including non-interest (off-balance-sheet and fee) income and non-interest (operating) costs, the Lerner index is better suited to capture competition in broader banking activities. As an alternative, we use the net interest margin because competition in traditional activities that dominate less developed banking systems is best reflected by the spread between lending and deposit taking activities. Since our dataset contains numerous emerging markets, relying on the net interest margin provides a sensitivity check. Further, Gropp, Hakenes, and Schnabel (2011), in their analysis of the effect of public bailout guarantees, show that the channel making protected banks’ competitors more aggressive runs via interest margins. The two measures are not significantly correlated, the coefficient is -0.014.

The key explanatory variables are coded as binary variables that take the value of one in the year the intervention was announced and in subsequent years if a country was still affected by the intervention as detailed by Laeven and Valencia (2008, 2010, 2012). Our approach is identical to the coding suggested by Dell’Ariccia, Detragiache, and Rajan (2008). We register 11 blanket guarantees between 1996 and 2010. The dummy for liquidity support takes on the value one if liquidity support provided by the central bank is at least 5% of deposits and liabilities to nonresidents/GDP (34 instances). Our dummy for recapitalizations is restricted to significant recapitalizations, defined as recapitalizations whose costs exceed 3% of GDP. We register 32 recapitalizations. We code takeovers of systemically important banks and instances where the government takes a majority stake in banks’ equity capital as nationalizations. Our data show 26 cases of nationalizations.

# _3.1 Preliminary inspection_

In a preliminary data inspection, we demonstrate separately for each country that announced blanket guarantees, liquidity support, recapitalizations, and nationalizations the change in the average Lerner index in Figure 1, and in the net interest margin in Figure 2. We also show the corresponding change for all countries in the control group over the same period. Each panel illustrates the effect of the respective government action. Countries with interventions are represented by a triangle, and countries in the control group are depicted by a square. For example, Thailand issued a blanket guarantee in 1997 (shown at the bottom left-hand corner in Figure 1 in the panel with blanket guarantees). In this year, the Lerner index dropped by 0.14. At the same time, the control group (defined as countries not having 8
24 _* What role does the policy framework of an economy playfor economic growth?_ As shown in Table 8, the index that represents the policy framework of the economy has an important role in ensuring not only the existence of convergence to the set threshold of 75 percent of the EU average income per capita, but also the number of years in which it will take place. For example, while for Poland convergence is presently not expected to take place, a higher value of this index will allow this country to converge. Also, most other countries may see the number of years required for convergence cut in more than half if the index is sufficiently high. As described in Section III, this index represents the policy framework of the economy and covers a broad range of issues; among them a country's monetary policy, fiscal policy, degree of openness, rule of law, and degree of price system distortion. The current value of this index for most CEE countries is well below that of the EU average country, which currently stands at 3.8 points (see Table 9). The sole exception is the Czech Republic whose current average is higher than the EU Average. Also, three of the components of the index for CEE countries are well below those of the EU average country. These are the degree of openness component, the indicators of monetary and financial/banking sector policies, and the degree of distortion of the 18 price system

**Table 8: Years for Convergence for Different Policy Framework Scenarios.**

||**Current**|**Status**|**Scena**|**rios**|||||
|---|---|---|---|---|---|---|---|---|
||Index|Years for|3.00|_3.50_|4.00|4.33|4.67|5.00|
||Value|Convergenc|e||||||
|Czech Rep.|<br>4.00|15|25|18|15|13|12|11|
|Hungary|3.10|41|44|33|27|25|23|21|
|Poland|2.95|###|###|71|49|42|37|33|
|Slovakia|_3.05_|41|42|31|26|23|21|20|
|Slovenia|2.65|91|54|37|30|27|24|23|

Note: ### denotes no convergence and implies that the country never reaches 75 percent of the EU average level of income per capita (i.e., the steady state of this country is below this threshold).

Scenario Assumptions: In all scenarios it is assumed that CEE countries maintain all their current growth deterninant values except that corresponding to the index that represents the existing "policy framework" (which Is Somewhat surprisingly, the index value for rule of law in CEE countries is not as low (relative to the EU average) as one would be tempted to expect. It is likely that this reflects the way in which this indicator is constructed. Johnson and Sheehy emphasize both excessive regulation as well as well-defined property rights.
3 coincided, and thu authors concluded tpat tuie clinic data were very useful for assessing nutritional status.

8. For Botswana, UNICEF/Cornell (1983) compared household survey data for the catchment areas of 22 clinics with the clinic data from two contiguous time periods. The average difference over these areas was that clinic data provided a lower es.imate of malnutrition than survey data by about 11 percent. There were, however, much larger differences in some areas. In about one third of the areas, the differences were less than ten percent; in a further third, the differences were between ten and twenty-five percent; and in the remaining third the differences were greater than twenty-five percent. The study concluded that the differences in estimate were important. Because clinic attendance at the time was 89 percent of children under five (food was being distributed to clinic attenders as a drought relief measure), the authors suggested that the differences were due not 0; sample error, but due to non-sample errors such as poor weighing and recording techniques, inaccurate clinic scales and the like.

9. For Swaziland, Serdula, et al., (1987), compared national household survey data with clinic dat., in four regions. In total, the estimate of the malnutrition rate among first time clinic attenders and the survey-based estimate were very close, and within the confidence interval of the survey estimate. The distribution by age was also very close. The regional distribution differs between the two, and the rates for children who have attended clinics more than once in a given year were much lower than for those who were attending the first time within the year. The authors concluded that surveillance data will not provide a valid estimate of nutritional status of the general population or of the difference between regions.

10. In order to add a further comparison to the literature, the next section presents the time series of clinic and household survey data available for Jamaica, and the general issues relating to sample bias are re-examined in light of that country's experience.

# Section III: The Jamaican Case

11. Health Care in Jamaica. The clinic data presented here are from the Ministry of Health clinics. Jamaica has a well developed public health care system which provides free preventive and primary services. Ninety percent of the population lives within ten miles of a health center. The lowest level center is staffed by a midwife and two community health care workers. They do some extension work for maternal and child health. Jamaica also has an active private health system focused largely on primary care. The infant mortality rate is 11 per 1000 live births,& vaccination coverage is 82 percent of children from one to four years for tuberculosis, 62 percent for measles, and life expectancy is 73 years.

_2./_ Ashley, gt al. calculate a peri-natal mortality rate of 38 per 1,000 live births, which is consistent with an infant mortality rate rather higher from the 11 used in the World Development Report. 1990.
**Table 3. Descriptive statistics of labor market variables.**

|||**Initialyear**|**2013**|**Change**|
|---|---|---|---|---|
|log(employed)|_mean_<br>_sd_|11.4<br>1.9|11.6<br>1.9|0.2|
|share temporary workers|_mean_|0.095|0.112|0.017|
||_sd_|0.136|0.134||
|share part‐time workers|_mean_|0.131|0.155|0.024|
||_sd_|0.156|0.160||
|share working from home|_mean_|0.126|0.154|0.028|
||_sd_|0.143|0.146||
|share self‐employed|_mean_|0.159|0.164|0.004|
||_sd_|0.169|0.167||
|share second job|_mean_|0.051|0.045|‐0.006|
||_sd_|0.046|0.042||

Note: based on labor market outcomes sectoral‐averages, EULFS 1995‐2013.

17
Figure 7 presents international comparison of debt rations across countries. Egypt falls below average of the international ratios. These patterns suggest that listed companies are not relying on debt as a significant source of their external finance. Figure 8 presents cross-country comparison of interest payments on debt. Egypt falls approximately in the middle of the distribution on this ratio.

**Figure 7.  Debt to Assets, Listed Firms Data**

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The demand for enforcement also might be higher for large and medium firms due to higher labor union representation. While the trade union density or coverage of workers has reduced from close to 100% in the Soviet era, it still remains high for large and medium firms. For instance, the largest trade union in Russia, FNPR, claims to represent 70 percent of the workers in the large and medium firms. Small firms, on the other hand, have few workers who are represented by the union (Gimpelson 2010; Ashwin and Clarke, 2002).

The Labor Code itself has specific restrictions that apply to larger firms. Article 217 of the code stipulates that all organizations with over 100 employees need to either setup a labor protection service or employ a labor protection expert. In addition, these organizations need to provide an elective trade union body and a “room and appliances” for its functioning, all free of charge (Labor Code, 2002).

# **3. Data**

## **3.1 Labor regulation enforcement**

The measures of labor law enforcement used in this paper were originally published in Vishnevskaya and Kapelyushnikov (2007), and subsequently discussed and analyzed in more detail in Gimpelson et al. (2010). The measures reflect the capacity of labor inspectorates and courts to enforce the regulation or judge disputes, and the demand/voice for enforcement as expressed by the number of cases filed. The authors collected these data for the years 2000‐2005 from the records of the Federal Labor Inspectorate and the Judicial Department of the Supreme Court. The measures are averaged over 2000‐2005, and as suggested by the authors, expressed per hundred thousand employees in large and medium firms, the population effectively covered by the regulation. Table 1 summarizes these measures, which cover 79 regions.

Our preferred measure of enforcement capacity is the number of labor inspectors per hundred thousand employees in large and medium firms. This captures the capacity of the labor inspectorate. Another such measure, the density of inspections, is highly correlated with the density of inspectors and does not add much information (Table 2).

We also present regressions in which enforcement is measured by the number of dismissal dispute cases filed (by workers) per hundred thousand employees in large and medium firms. This a broader measure as case filing by workers should, among other factors, depend on their perceptions of how efficiently the legal system will deal with a dismissal related dispute. It could also reflect a perceived pro‐worker bias of local courts. Higher worker demand for legal redress is also expected to raise firms’ assessment of the probability of enforcement of severance payments.

Using this alternative measure does not change our main results. This is not surprising because it is highly correlated with the density of inspectors. Though it adds another dimension to the measurement of enforcement and as such, serves as a robustness check, it is not our preferred measure. Its interpretation is less clear than that of LI capacity; moreover, the number of cases filed could mechanically depend on 6
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under communism before the transition. Most of the foreign trade was accomplished within the socialist block. Starting as early as 1989, they began the transition from central planning to market economy. However, there is still much difference as to the exact trajectory they followed during the reform. While many CEECs adopted a comprehensive economic and political restructuring with precipitate privatization process, China was more prone to focusing on economic reforms alone. This discrepancy, together with other factors such as different host country’s comparative advantages contributes to the variation in FDI characteristics and trade pattern between the two regions. As pointed out in Sachwald (2004), when comparing FDI from US, Japan and the EU, they found that most US and Japanese multinational corporations (MNCs) are interested in taking China as the manufacturing platform via vertical channels while their European counterparts more favor a horizontal linkage in its domestic market. Surveys on international investors also indicate that, among the top decisive factors in determining FDI destination, relatively high skilled labor force in line with production needs in CEECs is the most attractive attribute that drives their investment to the region.

Meanwhile, the fifth EU enlargement<sup>2</sup> with accession to the Central and Eastern European countries brings the opportunity for European investors to extend their operations in new member states. With access to a larger market and free movement of capital and labor resources across member states, foreign investors are more inclined to participate in local economy for reason of comparative advantages. Early literature has empirically testified the positive impact of increased integration in the European Union. Norman (1995) showed that the process of regional integration in the EU resulted in a

> 2 The European Union has undergone five enlargements with the largest occurring on May 1, 2004, when 8 out of 10 new members are CEECs, including Czech Rep, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia and Slovenia.

8
**Notably, the considerable expansions in the real minimum wage have been accompanied by a rise in the ratio of minimum wage to median wage, a measure of the relative importance of the minimum wage in the labor market.** This ratio now positions Romania at mid-ranking among OECD nations and is elevated compared to other EU countries. From 2007 to 2017, the minimum-to-median wage ratio surged from 38% to its peak of 60%, placing Romania among the upper ranks of the EU. During these years, Romania transited from the bottom to the top in the ranking of the EU member states by this ratio (minimum-to-median wage). Subsequently, post-2017, this ratio experienced a slight decline, settling at 55%. Similar trends are observed when looking at the minimum-to-mean ratio (figure 2, panel a). Despite slower minimum wage growth post-2017, Romania is still ranked at the top. In 2023, among European OECD countries, only France, Portugal and Slovenia have significantly higher minimum-to-median wages than Romania. In Poland and Luxembourg, these values are similar to those in Romania (figure 2, panel b).

**This ratio is relevant, as it has been frequently used to assess the constrictive nature of minimum wage policies (ILO, 2012).** When the minimum wage is high relative to the median (the so-called Kaitz index (KI)), it is expected to have a more significant bite or stronger “disemployment”<sup>5</sup> effects on the labor market (Medrano-Adán & Salas-Fumás, 2023; Cengiz et al., 2019). Ahlfeldt et al. (2022) found that beyond 50% of the mean wage, increases in the minimum wage result in a progressively greater reduction in employment. Rutkowski (2003) <mark>suggests</mark> this ratio should be at most one-third in countries with elevated unemployment among young and low-skilled workers. In 2022, Romania's unemployment rate for individuals aged 15 to 74 fell within the middle range compared to other European Union (EU) member states. Specifically, it was 5.6%, whereas the EU average was 6.2%. However, the youth unemployment rate for those aged 15 to 24 was relatively high in Romania at 22.8%, surpassing the EU average of 14.5% (Eurostat, 2024).

> 5 "Disemployment effects" is a term commonly employed in minimum wage studies, denoting adverse impacts on employment levels.

5