rafmacalaba/datause-encoder-data huggingface dataset:
contracts of employment, and of employment protection legislation. We use the aggregate indicator which combines information on (1) valid grounds for dismissals, (2) prohibited grounds for dismissals, (3) probationary period, (4) procedural notification requirements for dismissals, (5) notice periods, (6) severance pay, (7) redundancy pay and (8) avenues for redress.<sup>16</sup> The EPLex aggregate indicator has a scale from 0 (lowest level of employment protection) to 1 (highest level of employment protection). To facilitate the interpretation of the results, we use a dummy variable equal to one if the EPLex is above the median value for this sample. The actual values of the EPLex for our sample are displayed in Table 1. Unfortunately, the index is not available for some countries, which are excluded from the estimation sample.<sup>17</sup> Table 6 shows the estimates of equation (1) interacting the main effect with the dummy variable indicating high level of employment protection. Column (1) shows that the negative impact of the credit crunch was higher in countries with higher levels of employment protection. More specifically, while total employment declined by an additional percentage point among high FD sectors in countries with low levels of employment protection, that figure is about 6.7 percentage points in countries with high levels of employment protection. To a large extent, this effect seems to be driven by the impact of the credit crunch on the employment of temporary workers. As seen in column (2), while the effect of the credit crunch on both temporary and permanent workers was rather similar in countries with lower levels of employment protection (in the sense that their shares in total employment did not significantly change), the impact on temporary workers was negative and significant only in countries with high levels of

> 16 ILO (2015) provides a full description of the index and methodology.

> 17 Ideally, we would have liked to include a measure of the EPL for temporary workers as an additional interaction term in these regressions. However, the EPLex counterpart for temporary workers is not available for our sample of countries.

16
# **Table 4: Mean Characteristics of Students Participating in NAfKE in Urban Areas of Amman, 2006** **<u>and 2008</u>**

|**_2006_**|_Discove_<br>_ry_|_Grade 5_<br>_Non-_<br>_Discovery_|_Total_|_Discove_<br>_ry_|_Grade 9_<br>_Non-_<br>_Discovery_|_Total_|_Discove_<br>_ry_|_Grade 11_<br>_Non-_<br>_Discovery_|_Total_|
|---|---|---|---|---|---|---|---|---|---|
|Attended preschool|77%|64%|74%|66%|54%|64%|67%|56%|64%|
|Have repeated classes|12%|14%|12%|6%|8%|7%|3%|3%|3%|
|Number of people living at home|6.7|7.6|6.9|6.9|8.1|7.2|6.4|8.2|6.8|
|Mother with secondary education|36%|32%|35%|46%|42%|45%|45%|44%|45%|
|Mother with college education|21%|14%|20%|18%|16%|17%|20%|15%|19%|
|Mother with first grade of<br>university education|21%|10%|19%|13%|5%|12%|13%|5%|11%|
|Mother beyond first-grade<br>university|0%|0%|0%|0%|0%|0%|0%|0%|0%|
|Parents prefer academic track|77%|81%|78%|89%|84%|88%|97%|94%|96%|
|Principal has master’s degree|24%|20%|23%|33%|20%|30%|15%|36%|20%|
|Principal undergoes training in<br>administration|65%|50%|62%|64%|76%|66%|64%|39%|58%|
|Number of principals in the last 5<br>years|2.3|15.1|4.8|5.4|6.4|5.6|3.9|6.9|4.7|
|Parents meet with principal to<br>discuss academics|73%|64%|71%|77%|76%|77%|82%|74%|80%|
|Corporal punishment|46%|68%|51%|41%|61%|45%|23%|24%|23%|
|Teacher with teaching experience|12.8|11.4|12.5|12.3|11.9|12.2|13.8|9.4|12.7|
|Teacher with college degree|18%|20%|19%|5%|6%|6%|0%|0%|0%|
|Teacher with bachelor’s degree|67%|77%|69%|73%|86%|76%|74%|84%|76%|
|Teacher with bachelor’s degree<br>plus education diploma|11%|3%|9%|13%|9%|12%|18%|12%|17%|
|Teacher with master’s degree|4%|1%|3%|7%|0%|6%|7%|3%|6%|
|Teacher with Ph.D.|0%|0%|0%|0%|0%|0%|0%|2%|0%|
|Teacher with Arabic major|64%|42%|60%|60%|51%|58%|59%|45%|55%|
|Teacher in the field Arabic<br>language|19%|37%|24%|13%|22%|15%|8%|1%|6%|
|Teacher with math major|62%|57%|61%|47%|72%|52%|70%|47%|63%|
|Teacher in the field of math|30%|13%|25%|13%|3%|11%|9%|9%|9%|
|Educational software available|34%|13%|29%|42%|19%|38%|32%|14%|28%|
|Computer available for classes|48%|11%|41%|53%|30%|48%|53%|41%|50%|
|Teacher has International<br>Computer Driving License|64%|34%|58%|71%|54%|67%|70%|65%|69%|
|Teacher has Intel certification|22%|10%|19%|36%|19%|32%|33%|27%|32%|

Source: Authors’ calculations, based on NAfKE 2006 and 2008 surveys 9
For each participating installation, an account is set up in its national emissions trading registry to record the issuance, transfer, cancellation, retirement and banking of allowances. All national registries are also connected to a central registry at the EU level—the Community Independent Transaction Log (CITL). The centralized registry tracks the ownership of allowances across the entire carbon market. Using information presented in CITL, we are able to identify participants and non-participants in the three trading sectors under study. We then choose firms that are similar in size (measured by turnover _before_ the program) to form our treatment and control groups. More details on data construction are explained in the next section.

Another key element of EU ETS is the initial allocation of emission allowances. During the first two trading periods, the program gives national governments substantial discretion in determining the allocation and distribution of allowances across sectors, subject to general guidelines by the European commission<sup>10</sup> . During 2005-2012, over 90 percent of the allowances were given out for free to trading sectors based on burden sharing obligations under the Kyoto Protocol, and individual installations’ historical emissions and projected abatement costs. Early evidence suggests that at the aggregate level power generation was the only sector receiving fewer allowances than verified emissions (Ellerman and Buchner, 2008). The power sector faced more stringent regulation because it is thought to have more low-cost abatement options and is less exposed to international competition.

Despite evidence of the over-allocation at the sector level, firms still have incentives to cut emissions since they can sell excess permits at the ongoing market price. Indeed, several studies suggest that CO2 emissions dropped by around 3 percent relative to a baseline without ETS during the pilot phase (Ellerman and Buchner 2008; Ellerman, Convery and de Perthuis, 2010; Anderson and Dimaria, 2011), and the first two years of Phase II (Abrell et al., 2011). There are several ways through which power utilities and industries can mitigate emissions. These include switching to low-carbon fuels, optimizing production processes, and investing in more efficient equipment and low-carbon technologies.

> 10For the third trading period beginning in 2013, there will no longer be any national allocation plans. Instead, the allocation will be determined directly at the EU level.

6
## **Macroeconomic variables**

- **Interest rate risk** : Uncertainty in market rates is expected to translate into higher spreads as banks try to hedge interest rate risk by applying a risk premium. We use the NBT refinancing rate (in level) to proxy for interest rate risk and expect a positive sign for this variable.

- **Inflation:** This is a widely used variable to proxy for macroeconomic volatility. It is expected to have a positive relationship with net interest spreads because higher inflation introduces economic uncertainty that needs to be compensated for by higher margins. We use quarterly changes in the Consumer Price Index rate.

## **Institutional variable**

- **Legal environment** investor protection and bottlenecks in the credit infrastructure, especially insolvency and debt resolution. To reflect this feature in the empirical analysis, we use the Regulatory Quality index compiled by Kaufmann, Kray and Mastruzzi (2006). This is an index capturing perceptions of the ability of the government to formulate and implement sound public policies and regulations. The index, which ranges from –2.50 (weak) to 2.50 (strong) covers general aspects of government regulation in all areas of economy (not only banking) that permit and promote private sector development. A higher index value indicates better regulatory quality and is expected to be associated with lower margins; hence, we expect a negative sign for this variable.

Figures 5 to 14 provide an overview of the independent variables used in the analysis (quarterly banklevel data are weighted averages, with weights corresponding to banks’ total assets).

# IV. ESTIMATION RESULTS

We present the estimation results of Equation 1 in three steps. In the first step, we run the model on only the bank-specific variables (Table 1, columns 1–4). In the second step, we run the model, including the macroeconomic variables (Table 1, columns 5–8). In the third and final step, we run the full model (Table 1, columns 9–12).<sup>6</sup> As Table 1 shows, the R-squares for the three steps are practically the same, suggesting that bank-specific variables explain almost all the variability in banks’ net interest margins.

As expected, less efficient banks charge higher interest spreads. The coefficient for the operating costs variable is positive and highly significant, indicating that a bank with an inferior organizational structure and less efficient management will pass its higher overhead on to consumers through wider margins. A one percentage point decrease in operating costs (as a share of total assets) is associated with a reduction of almost 500 basis points in the spread, holding all other factors constant. This result is

> 6 As a robustness check, we estimate the model using the FGLS procedure, the regression with Driscoll-Kraay standard errors, and the dynamic system GMM estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998). The results, which are available upon request, are fully aligned with our baseline model.

13
A similar trend is observed for firms in services, with the number of such firms in the north gradually converging with that in the south except between 2005 and 2014.

# **Regional Trends in Mining and Manufacturing in the North and the South**

We look at regional trends for subsectors within industry, specifically mining and manufacturing. For this, we use data from 2003 NIC and 2014 IBES. The 2003 NIC only surveyed industrial firms. Given this, it is not feasible to examine the number of firms and employment in agriculture or services over time.

Table 1 reports the numbers of firms and employees in 2003 and 2013 at the national level, in the north, and in the south. At the national level, the number of firms engaged in mining increased from 125 in 2003 to 294 in 2013 and the number of firms engaged in manufacturing increased from 23,797 in 2003 to 101,789 in 2013. The numbers of firms and employees remain overwhelmingly large in the south in both manufacturing and mining, compared to numbers for the north. The change in the number of mining firms and of employment in the south is higher than in the north by 14 times and 62 times, respectively. The south’s share of mining firms increased from 82 percent (103 out of 125) in 2003 to 89 percent (261 out of 294) in 2013, and its share of mining employment continued to dominate, totalling an overwhelming 98 percent in both years. This is also evident from figure 3 (panels b and c). On the other hand, the north’s share of manufacturing firms grew from 19 percent (4,623 out of 23,797) in 2003 to 31 percent (31,281 out of 101,789) in 2013. Similarly, its share of manufacturing employment grew from 9 percent (10,386 out of 116,774) in 2003 to 16 percent (44,507 out of 271,863) in 2013. The evidence suggests manufacturing employment in the north has partially converged with that in the south over time.<sup>6</sup> We next look at regional trends in manufacturing subsectors. We distinguish between heavy manufacturing and light manufacturing.<sup>7</sup> Given the importance of food and clothing, we also examine them separately in selected analyses. Table A1 compares growth of firms and growth in the number of employees (“growth of employees”) at the district level between

> 6 Data are not available for nonindustrial sectors in NIC 2003. However, based on comparable national household sample surveys for 2005/06, 2012/13, and 2016/17 (rounds of the Ghana Living Standards Survey), services employment in the north grew faster than that in the south, with the north’s share of services employment increasing from 43 percent to 45 percent; in comparison, manufacturing-employment growth was much slower in the north (Paul and Raju 2021).

> 7 Heavy manufacturing refers to manufacturing of coke and refined petroleum, chemicals, basic metals and fabricated metal, machinery and equipment, motor vehicles, and other transport vehicles, among others. Light manufacturing includes manufacturing of wood, paper, printing and reproduction of recording, pharmaceuticals, rubber and plastic, other nonmetallic items, computers, electronics, electrical equipment, furniture, food, and clothing.

9
# 3.2.   Descriptive statistics

Conflict has increased in all regions as measured by the mean number of events a household has experienced. Figure 1 shows us the evolution of conflict events between 2010 and spring 2017. Figure 1, panel a, displays the data of all events as well as property- and violencerelated events. We can see that from 2012 onward, the level of household victimization has greatly increased.

Figure 1, panel b, displays the mean number of events per year by the most common types of perpetrators: insurgents, bandits and criminals, and pastoralists and nomads. We can see that events in which the perpetrator was an insurgent peaked in 2014, which corresponds to the most violent year of the Boko Haram insurgency. The number of events involving bandits and criminals is increasing over time. The number of events involving pastoralists and nomads increased in 2013 and has remained at that level. It is noteworthy to point out that the decrease in 2017 is not representative of the entire year but only until May 2017 (see figure 1, panels a and b). It is meaningful to study the perpetrator and the type of event separately because both violent and property-related events are perpetrated by all types of perpetrators.<sup>11</sup> There are potential limitations in measuring victimization that merit discussion. If past events are not remembered as well as more recent events, our data might suffer from recall bias. To address this issue, we compare the time distribution of victimization to the conflict intensity from the ACLED database at the annual level. Figure B.1 in appendix B displays the time distribution of the events; the vertical axis shows what fraction of all events recorded between 2010 and 2016 for the data set in question happened in each specific year. This way of analyzing the data yields a meaningful comparison between our data set and the ACLED database, as ACLED collects data on the number of conflict events and fatalities reported by newspapers and thus without recall bias. However, the different measures mean we cannot compare _the levels_ of conflict across the data sets. Nonetheless, we can see that the time trend in both conflict events and fatalities in the ACLED is similar to the time trend of our measure of victimization. Although it is impossible to fully rule out recall bias, we are not too worried

> 11 Although violent events are most often perpetrated by insurgents (30.5 percent of all violent events between 2010 and 2016), bandits and criminals and pastoralists and nomads are often reported as perpetrators of violence (18.5 percent and 22.8 percent, respectively). Similarly, whereas property-related events are most often perpetrated by pastoralists and nomads (38.7 percent), 20.5 percent and 22.3 percent of property-related events are perpetrated by insurgents and bandits and criminals, respectively.

10
# GHANA: DEFICIT CONCEPTS

## 1980 -1987

<!-- Start of picture text --> 8-<br>6 Primary<br>* Operational<br>4 Cash<br>2- -<br>.2 -<br>4 -4<br>-6<br>-8<br>-10-<br>1980 1981 1982 1983 1984 1985 1986 1987<br>Calendar Years<br><!-- End of picture text -->
# **Findings**

Table 3 shows OLS estimation results for the basic OLS model for both DPT and measles. In the basic model without CRC status, the control variables for the most part take the expected sign – national income, bureaucratic quality, democracy, population density, and the linear trend variable (year) are all positive in columns one for DPT. In column seven for measles, GDP is negative (though not significant), and democracy has no effect. The key variable of interest, CRC ratification, is negative and significant at p=0.05 for DPT (column 2) and negative and significant at p=0.10 for measles (column 8). This suggests that ratification is correlated with lower than expected immunization rates in subsequent years, all other things being equal. But the data reveal an interesting pattern if the sample is split by income level. For both DPT and measles, CRC has a negative coefficient in the case of low and lower middle-income countries, but positive coefficients for upper middle and high-income countries. Only the coefficient for lower middle-income countries is significant at p=0.05 for DPT, but three of the four income level subsamples are significant at that level for measles. Note that country fixed effects and a year trend are included in these models, so the significance of ratification variable is not merely a reflection of a global trend.

Table 4 subjects these results to IV estimations, controlling for the endogeneity of the ratification decision with average regional ratification rate in a given year and legal origin as instruments. Again, the CRC variable is dichotomous for each country-year: it is 0 if a country has not ratified in a given year and 1 if it has; and the estimations include country fixed effects and a simple annual trend variable. The key finding from this table is that the IV estimations replicate the finding from OLS regressions: although the overall effect of CRC ratification is negative (though not significant, see columns 1 and 6), the effect of ratification is negative for low and lower middle-income countries but positive for upper middle and highincome countries. This time, the coefficient on CRC ratification is significant at p=0.05 for both the upper middle and high-income countries for DPT and measles. It appears that the effect of CRC ratification is 12
getieral labor market is at least six years younger. RFFSA's workers were paid between ten and thirty percent more then the average worker in the respective labor market.

**Table 3: PROFILE OF RFFSA'S LABOR FORCE (NOVEMBER 1995)**

||_45_<br>|18|67|25|7|1,215|
|---|---|---|---|---|---|---|
|iMi4wes|t40|17|38|_54_|8|1,186|
||4 1|18|38|_52_|_9_<br>I|<br>1,656|
|**0S~h**<br>|40<br>|18<br>|66|-<br>26|8|1,106|
|-<br>**-Nxba**|**ro**a40|<br>16|33|47|20|1,232|
|||41|**1**7<br>9<br>|1<br>88|3|1,302|

The emerging concern was then that without some assistance, many of the rail workers declared redundant were likely to find it difficult to compete in the labor market in the short run. Even in the event these workers did manage to reenter the market, chances would be that they would be paid less. The view at that tirne was that what was needed was enough training to reduce the cost imposed by specialized job experience and the lack of formal education. A teatmi of advisors from various training institutions was convened by RFFSA to prepare a menu of options for affected employees to choose from and to design training packages that would meet the employees' needs.

# 5 - A **Generic approach** to jointly **address efficiency and** social **concerns**

The next step was for RFFSA to work on building a staff reduction program that would address its social concerns as well as the efficiency requirements of the restructuring process. This meant that the legally-required severance payments (which, on average, corresponded to the euuivalent of ten months salary) was not enough. In addition, the program had to fincd a way of encouraging early retirement to reduce the average age in the company, and it had to provide some incentives for voluntary retirement to minimize the number of workers that would have to be fired. These incentives had to be complemented by training programs, based on regional employment opportunities, and outplacement assistance that would reduce the personal drama of the workers declared redundant.

These ideas were transformed into the following specific goals of the staff reduction program. The program was to: (a) reduce RFFSA's employee headcount by about 18,000 over three vears, in addition to the 4,000 employees who had applied for early retirement in 1995; but it also had to: (b) increase staff productivity through training and outsourcing; and (c) minimize the social cost of the adjustment through appropriate severance payments. The
Policy Research Working Paper 5391

# **Abstract**

Public debt has surged during the current global economic crisis and is expected to increase further. This development has raised concerns whether public debt is starting to hit levels where it might negatively affect economic growth. Does such a tipping point in public debt exist? How severe would the impact of public debt be on growth beyond this threshold? What happens if debt stays above this threshold for an extended period of time? The present study addresses these questions with the help of threshold estimations based on a yearly dataset of 101 developing and developed economies spanning a time period from 1980 to 2008. The estimations establish a threshold of 77 percent public debt-to-GDP ratio. If debt is above this threshold, each additional percentage point of debt costs 0.017 percentage points of annual real growth. The effect is even more pronounced in emerging markets where the threshold is 64 percent debt-to-GDP ratio. In these countries, the loss in annual real growth with each additional percentage point in public debt amounts to 0.02 percentage points. The cumulative effect on real GDP could be substantial. Importantly, the estimations control for other variables that might impact growth, such as the initial level of per-capita-GDP.

This paper—a product of the Economic Policy Sector, Poverty Reduction and Economic Management in Latin America adn the Carribbean Region—is part of a larger effort in the department to understand the impact of indebtedness on economic growth. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at fkoehler@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team