rafmacalaba/datause-encoder-data huggingface dataset:
33 Table 22: Profits and Managerial Compensation

|Profits<br>(zloty billion)|1990<br>_<br>(%)||<br>19911<br>M()|
|---|---|---|
|_0-0.5_|1|1|
|_0.5-1_|_0.5_|_0.5_|
|_1-5_|0.2|0.2|
|5-10|0.1|0.1|
|10-20|0.06|0.05|
|20-30|0.04|0.02|
|30-40|0.02|0.01|
|40-50|0.01|0.005|
|50-100|0.005|0.001|
|> 100|0.0001|0.0001|

# Note: The profit base for this calculation is Profit II - PPWW.

An example will show the extremely weak link between profits and managerial reward. In 1990, if the firm registered $ 50 million of profits (before tax - PPWW), the manager would get about $ 3800 for the year. In 1991, this drops to about $ 2800 for the year. In a case where the manager's basic wage is 7 times the average wage in the firm, equivalent compensation can be obtained by a one-time rise in monthly wages of about $ 45 per month in 1990 and only $ 33 in 1991. Figure 13 plots this link, with profits in dollars shown on the x-axis and resulting bonus, also in dollars, on the y-axis. This weak link between profits and bonus (which reaches an asymptote of about $ 3900 in 1990 and $ 2900 in 1991) creates an automatic pre-disposition towards wage increases and emphasizes the short-run. This is obvious when contrasting the present system of managerial compensation (MC) in equation (1) with a system where the link to average wages is broken and that to profits is strengthened:

- (1) MC = K.w + L.profits, K a given multiple, w the average wage and L some percentage

- (2) MC = B + C.profits, C a specified percentage greater than L.

Regarding (1), it is obvious that if K is large enough and L is very small - the case obtaining in practice - an increase in wages will lead to larger managerial compensation. Adopting a scheme like (2), which completely de-links compensation from average wages, solves the problem of immediate compensation; but the issue of long-run compensation linked to maximizing firm value remains.
|Non-agricultural self-employment|||0.09440**||||0.09455**||||0.09433**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||(0.04186)||||(0.04185)||||(0.04185)||
|Remittances & Others|||-0.00271||||-0.00267||||-0.00273||
||||(0.09576)||||(0.09580)||||(0.09574)||
|Property income and investments|||0.20178**||||0.20208**||||0.20163**||
||||(0.08368)||||(0.08371)||||(0.08367)||
|Transfers and other benefits|||0.11352***||||0.11366***||||0.11345***||
||||(0.04169)||||(0.04171)||||(0.04166)||
|Own > median size of land (2.5 acres)|||0.20003***||||0.19996***||||0.20006***||
||||(0.03193)||||(0.03193)||||(0.03193)||
|Observations|5,896|5,893|4,128|4,125|5,896|5,893|4,128|4,125|5,896|5,893|4,128|4,125|
|Kleibergen-Paap rk Wald F|17.77|17.77|13.59|12.71|21.52|21.51|15.60|14.48|14.43|14.44|11.71|11.04|
|Root MSE|0.591|0.591|0.535|0.362|0.591|0.591|0.536|0.364|0.591|0.591|0.534|0.362|
|**_Included controls_**|||||||||||||
|Exogenous controls (age, agesq, sex<br>“male==1”)|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|
|Other controls (HH size, marital status,|||||||||||||
|education, occupation, other income sources,<br>|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|_No_|_No_|_Yes_|_Yes_|
|land ownership)|||||||||||||
|Year fixed effects|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|Cluster fixed effects|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|_Yes_|_Yes_|_Yes_|_No_|
|Household fixed effects|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|_No_|_No_|_No_|_Yes_|

22
Table 5: Teachers’ heterogeneity on the impact of the school shutdowns on students’ learning, fifth-grade (2007-2009)

|**Efects of school shutdowns on **|**the locally-ma**|**naged net**|**work**||||||
|---|---|---|---|---|---|---|---|---|
||Math||||Portugue|se|||
||DiD|DiD|DiD|DiD|DiD|DiD|DiD|DiD|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|H1N1|-2.75**|-3.04|-2.28|-6.24|-0.54|0.12|-0.24|2.74|
||(1.23)|(2.27)|(1.36)|(2.59)|(0.95)|(2.11)|(1.14)|(2.65)|
|H1N1 versus||0.01||||-0.03|||
|students per teacher||(0.11)||||(0.11)|||
|H1N1 versus|||-1.94||||-1.04||
|teacher absenteeism|||(1.17)||||(1.27)||
|H1N1 versus teachers that||||0.04|||||
|correct Math homework||||(0.03)|||||
|H1N1 versus teachers that||||||||-0.04|
|correct Portuguese homework||||||||(0.03)|
|N. schools|3912|3912|3886|3912|3912|3912|3886|3912|
|Adj. R2|0.8|0.8|0.8|0.8|0.8|0.8|0.8|0.8|
|**Efects of school shutdowns on **|**the state-man**|**aged netw**|**ork**||||||
||Math||||Portugue|se|||
||Triple|Triple|Triple|Triple|Triple|Triple|Triple|Triple|
||DiD|DiD|DiD|DiD|DiD|DiD|DiD|DiD|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|H1N1|-4.25***|-3.88***|-3.72***|-15.71***|-2.73***|-2.32*|-2.26**|-7.05**|
||(1.06)|(1.49)|(1.11)|(3.88)|(0.90)|(1.32)|(0.94)|(2.92)|
|H1N1 versus||-0.03||||-0.03|||
|students per teacher||(0.06)||||(0.05)|||
|H1N1 versus|||-2.08**||||-1.74**||
|teacher absenteeism|||(0.94)||||(0.80)||
|H1N1 versus teachers that||||0.14***|||||
|correct Math homework||||(0.04)|||||
|H1N1 versus teachers that||||||||0.05|
|correct Portuguese homework||||||||(0.03)|
|N. schools|5329|5329|5295|5329|5329|5329|5295|5329|
|Adj. R2|0.7|0.7|0.7|0.7|0.6|0.6|0.6|0.6|

_Notes_ : The authors’ estimate is based on data from _Prova Brasil_ , Census of Education, and IBGE. Wild bootstrap p-values. 95% CI in brackets. ***, **, and * indicate significance at the 1, 5, and 10% critical levels. Standard error in parenthesis and clustered at the municipality level. All regressions are weighted by fifth-grade enrollment at the school level. Math performance on a scale from 0 to 350 (SAEB scale). reading performance on a scale from 0 to 325 (SAEB scale). Columns DiD show the estimates of differences-in-differences as described in subsection 4.0.1 and Columns Triple D show the estimates for triple difference-in-differences as described in subsection 4.0.2. The sample of municipalities and schools included in the analysis are detailed in Table 2.

25
At the time of closing the program (February 1997), only four firms had "graduated": two were privatized in 1996, while another two were liquidated. Another 13 enterprises from the program were closed down by the new government in August, 1997. This closure was not related to the isolation program, as some other companies (not on the isolation list) were also closed. The program did not have a significant impact on managerial turnover: during the implementation of the program only seven (of 147) CEOs were fired. Although these outcomes were hardly what the architects of the isolation program had in mind, perhaps the program was effective in operational turn-around and eliminating the dependency of isolated firms on the state budget. We consider the empirical evidence in the following sections.

# **IV. The Data**

I have firm-level data (balance sheet and profit and loss statements) for 1992-96 obtained from the Romanian Statistical Office. The data are annual observations and cover all firms which were registered as state-owned enterprises in 1992. If some plants are owned by the same parent company, this relationship is accounted for in the data. Overall, more than 8,000 enterprises are included. From those, I select two groups of firms. The first set contains 146 firms from the isolation program, identified by firm numbers - one utility company was excluded from the study for lack of accounting data. The second set contains a control group of 146 firms, matched one for one with enterprises in the isolation group. Within each sector, I chose firms in the control group which had the most similar size (measured by number of employees), profitability, and subsidies (as measured by their share in total revenues) in 1992 to corresponding firms in the isolation program. Firms come from nine sectors. This algorithm resulted in a distribution of firms across sectors (Table 1) which is identical between the focus and control groups. The large share of agricultural firms is due to the inclusion of pig and poultry farms in the reorganization program in 1994. Although numerous, these firms do not account for a large part of overall employment. The largest firms are to be found in coal mining and utility sectors.

International accounting standards were not introduced in Romania during the sample period. This is not a problem in the analysis here which compares relative performance across groups of firms in the same year (period). Several adjustments were made, however. Under the Romanian accounting system, for example, subsidies and production for inventories are counted as sales. Since data on sales, subsidies, and inventory changes are reported in all cases, I recalculate the revenue numbers to account for sold (rather than produced) output. Firm-specific output prices are not available. One option is to use output price indices at the industry level. This, however, limits the comparisons between firms within the same sector, given the likely variation in their pricing strategies. I hence develop the analysis on the basis of ratios of revenues and 6
- 3 -

literature (Holt et. al. 1960, Fair 1969), it is assumed that the cost of a man period is a function of the number of hours worked, possibly reflecting the existence of overtime payments when actual hours exceed a given norm ("standard' hours). As a result, unexpected upward shifts in domestic absorption will, if the wage function is strictly convex, lead to higher costs and lower exports.

The model is applied to Turkey and Morocco. Evidence on export supply behavior in developing countries is even more scarce and systematic than it is for industrial countries,2/ despite the undisputed role that supply factors play in determining export performance for these countries. Turkey and Morocco were selected because of the significant shift that both countries have undergone in their trade incentive structure.

This paper is organized as follows: Section 1 outlines the model and shows that even in an equilibrium model with market-clearing prices, domestic demand pressure can still play a role in the export supply decision. Section 2 offers a cursory review of export policy and performance in Turkey and Morocco and sets the stage for an econometric analysis of both the capacity and the export supply decision which is presented in Section 3. The last section offers some conclusions.

# 1. Modeling Export Supply

It is not difficult to understand why export supply studies have been few. The issues surrounding the behavior of exporters are inherently difficult and not yet fully understood. We know for instance that production for export depends on existing productive capacity, yet we do
<!-- Start of picture text --> 0.500<br>Density of non-RCA Density of RCA<br>0.450 product lines product lines<br>0.400<br>0.350<br>0.300<br>0.250<br>0.200<br>0.150<br>0.100<br>0.050<br>0.000<br>37 29 81 86 79 89 32 33 49 96 18 71 44 23 4 60 20 61 8 7<br>Source: UN COMTRADE (2008), and World Bank staff calculations<br><!-- End of picture text --> Figure 5: Density of product lines with (blue) and without (red) revealed comparative advantage for Syria, 2 digit HS level, 2007. Selected HS codes reported on the horizontal axis correspond to slightly thicker bars.

export with revealed comparative advantage, the average density is 0.299. The overall distribution of product densities, as captured in Figure 5, reflects this distinction well.

It is especially interesting to observe that the density values for many of the sectors which have shown large increases in revealed comparative advantage over the past years are high. This shows that the Syrian economy has taken advantage of the factor substitutability between sectors with a strong export performance and sectors which had previously not found access to international markets. Some examples are miscellaneous chemical products (chapter 38), iron or steel articles (chapter 73), furniture (chapter 94), and miscellaneous manufactures (chapter 96). Other sectors with high density values are live trees (chapter 6), food residue and waste (chapter 23), straw (46), miscellaneous edible preparations (chapter 21) and wadding yarns and twine (chapter 56). While these numbers may indicate export potential for Syria in these sectors, it is important to note that the analysis relies on proximity indicators between products which are computed at the global level. This, in turn, implies that there may be impediments specific to the Syrian economy, which may render their production and export unprofitable.

# **3 Potential Determinants of Sectoral Export Diversification in Syria**

In the previous section, we analyzed the patterns of export diversification in Syria after the initiation of the reform agenda with the 9th Five-Year Plan in 2000. We found that 22
Table 9. Relationship Between Inequality and Human Capital (Excluding Top or Bottom 5<sup>th</sup> Percentile of Inequality)

|Dependent Variable is:|<br>|Share of Population<br>|Tertiary Education<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Inequality Variable is:|Market Gini|Net Gini|Market Gini|Net Gini|
|Sample:<br>Inequalityit|Excluding Top 5<br>0.61***|<sup>th</sup>Percentile<br>Panel<br>0.64***|Excluding Botto<br>A: 2SLS<br>0.48***|m 5<sup>th</sup>Percentile<br>0.56***|
||(0.16)|(0.13)|(0.19)|(0.14)|
|Inequalityit|-0.07***|-0.09***|-0.05*|-0.07***|
|*ln(yit-1)|(0.02)|(0.02)|(0.02)|(0.02)|
|ln(yit-1)|0.02***|0.03***|0.02***|0.02***|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Country FE|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|
|Observations|736|736|736|736|
|||Pane|l B: LS||
|Inequalityit|0.48***<br>(0.18)|0.53***<br>(0.16)|0.31<br>(0.19)|0.43**<br>(0.18)|
|Inequalityit|-0.05**|-0.07***|-0.03|-0.06**|
|*ln(yit-1)|(0.02)|(0.02)|(0.03)|(0.03)|
|ln(yit-1)|0.02*|0.03***|0.02**|0.03**|
||(0.01)|(0.01)|(0.01)|(0.01)|
|Country FE|Yes|Yes|Yes|Yes|
|Time FE|Yes|Yes|Yes|Yes|
|Observations|736|736|736|736|

Note: The method of estimation in Panel A is two-stage least squares; Panel B least squares. Bootstrapped standard errors are shown in parentheses. The instrument for _Inequality_ is _Residual Inequalityit = Inequalityit – αln(yit)_ , where α measures the effect that ln(yit) has on _Inequality_ . *Significantly different from zero at the 10 percent significance level, ** 5 percent significance level, *** 1 percent significance level.

34
90<sup>_th_</sup> quantiles, and is not significant. Similarly, the positive correlation of the share of employees with degrees and quality increases in magnitude and significance as we move up the distribution.

The contrary appears to be the case with ownership variables. The negative impact of being family owned or privately held is dramatically larger and significant only at the bottom quantile and has no impact at the median or 90<sup>_th_</sup> . This would suggest that these particular ownership structure are holding back firm management quality through the lower tail. Being founder owned appears to be an effect largely at the median, but not higher. Somewhat unexpectedly, having an external CEO has a negative effect, but only at the top quantile. This last result will not prove robust.

Competition continues to enter, and across the spectrum with the largest impact at the median. This suggests that competition is indeed important, but not especially through clipping the left tails. It is arguably a more uniform force that, as Bloom and Van Reenen suggest, may cause firms to work harder across the sample.<sup>14</sup>

# **5.2 The Global Sample**

Table 6 replicates the previous exercise, but using the entire sample of surveyed countries. With one important exception, the correlates that were significant in the original four country sample broadly enter in a similar fashion. On the human capital side, whether or not the manager has a degree enters significantly, and more or less uniformly, across the sample whereas before it affected the higher parts of the distribution. The share of workers with a degree also enters again strongly significantly, and of similar magnitude as with the Bloom Van Reenen sample. In addition, we find that whether the manager studied abroad now has a very significant impact and, again, particularly among the better firms. Further, the share of managers with a degree now also enters significantly with the highest correlation among the best firms.

> 14Since in our LDC sample, the effects of exporting are often stressed, we include this variable as well in separate specifications. However, we lose a substantial number of observations as it appears that in some waves and countries, this variable was not collected.

16
One potential issue for the interpretation of the results is the assumption of whether or not an ‘exit’ should be understood as a failure. Exits are often assumed to be failures; that firms were unable to continue in operations, either at all or at that location. Thus our interpretation of a weaker investment climate leading to greater exits is one where firms are either driven out of business or move to ‘greener pastures’ (i.e. it is not an instance of failure on the part of the firm, but on the part of the location to support that firm). However, it is possible that a subset of firms captured in our measure are in fact examples of success – i.e. firms exit because they were acquired or merged with another firm, and thus moved (possibly within the same city) with a new name.<sup>2</sup> For these firms, the quality of the investment climate did not detract from their performance; it is even possible that their success in an otherwise poor investment climate is exactly what made them attractive for acquisition.<sup>3</sup> Unfortunately we cannot know how many instances we have of this. But to the extent that ‘successful’ firms are in our pool of exiters, they would likely bias against finding significant results of a weak investment climate increasing exit rates. Table 8 shows robustness checks that look at subsets of exiting firms based on other information in the 2002 data to classify them as more likely to be failures. Table 8b looks at those firms that were already in arrears in 2002. Table 8c excludes those firms that are most likely to be ‘good performers/attractive targets’, i.e. those that are not in arrears and are above their city-sector median price-cost

> 2 Within this group of acquired firms, recall that those that continue to operate in the same location or with the same name remain in the sample as survivors.   This is most easily confirmed by the presence of 29 firms that were acquired by a foreign firm during the 3 intervening years (and answered both questionnaires).  These acquisitions are not represented as ‘exits’ in the data.

> 3 There is a hypothesis that in an area of corruption or lots of red tape is precisely when having a local partner is more desirable.  This is particularly true for multinationals that are likely to be less well situated to navigate this environment.  This can be tested for looking at whether JV or wholly owned foreign firms are more likely to be in locations of higher corruption and if JVs are then significantly less likely to exit from these locations.  The results do confirm that joint venture firms are significantly less likely to be impacted by the negative effects of cronyism, although other measures of corruption do not show any difference across JVs versus wholly owned foreign firms.

18
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$100,000 pesos per extra year of schooling under the most favorable supply-side scenario with zero discounting and assuming that the school building can be used for 40 years.<sup>29</sup>

# **Conclusions**

This paper has reviewed the evidence on MDG progress, its determinants, specific related policies, and resource requirements. We have introduced an analytical framework that has highlighted the key determinants of, and relationships between, social service utilization and MDG achievement. This framework has motivated our empirical review, which in turn focused on the effectiveness and efficiency of selected policies on the demand and supply sides of social service utilization in the health and education sectors.

The analytical framework has also suggested that MDG achievement may follow an s-shaped transition pattern in social service utilization and human development outcomes. Based on a simple regression analysis, we have illustrated that average MDG progress is likely to be too slow to meet most social sector targets in most developing countries. In addition, we show that MDG achievement, specifically primary enrollment rates and child mortality reductions, can be described according to an s-shaped transition path. These findings are generally in line with earlier evidence from Clemens et al. (2007), who have analyzed data for 1960 to 2000, although we only consider the past 20 years. More detailed analysis, however, reveals that the transition towards universal primary enrollment in poor countries with low initial enrollment appears to have accelerated considerably in the past 20 years. Furthermore, while average progress in child mortality reduction remains relatively slow, quite a number of countries have managed to achieve dramatic decreases in child mortality rates since 1990. These successes of the MDG process are typically rooted in specific policies and/or circumstances, which we have tried to identify in our review of the recent and rich empirical literature.

More specifically, our analytical framework led us to ask whether we can identify key factors and interventions for MDG achievement, and what can be said about supply- vis-à-vis demand-side factors in social service utilization. Furthermore, the framework raised the question of whether complementarities between different MDGs can be revealed empirically. We have indicated that providing definitive answers to these questions is impossible in light of the very diverse determinants, policies, and outcomes in the experiences reviewed. Nevertheless, we think some rules of thumb regarding the effectiveness and efficiency of specific types of policies can be discerned. These rules implicitly reflect what we regard as

> 29 The cost-benefit calculations presented are based on the assumption that the parameters of the estimated model, for example, a school-participation equation, are constant. There are good reasons to believe that this assumption is violated, at least for nonmarginal policy changes.

26