rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Board member distribution among bank groups in 1974**

|Bank group|(1)|(2)|(3)|(4)|_(5)_|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|Savings bank<br>Gi C|14<br>|4<br>|6<br>|29<br>|23<br>|7<br>|7<br>|66<br>|119|
|ro enters<br> <br>|10<br>|13<br>|16<br>|86<br>|72<br>|14<br>|_5_<br>|82|170|
|Large<br>6banks<br>Ril|6<br>|91<br>|100<br>|405<br>|320<br>|24<br>|19<br>|71|483|
|egonabanks<br>Pit bk|26<br>|61<br>|72<br>|252<br>|202<br>|16<br>|9<br>|_55_<br>|314<br>|
|rvae aners<br>Cti bk|23<br>|49<br>|38<br>|170<br>|137<br>|4<br>|3<br>|14<br>|185<br>|
|ooperave ans|8|2|4|19|16|2|4|31|73|
|Total|94|220|236|986|_6d_<br>(Ti."|67|47|319|1344|

Explanation of columns:

- (1): # of banks reporting Board members

(2): # of bankers holding the presidency of the Board (3): # of bankers holding the vice-presidency of the Board (4): # of bankers reported as Board members

- (2) to (4) refer to Boards of AG's

- _(5):_ # of firms with bankers on the Board (only AG's)

- (6) to (8) are the same as (2) to (4), but for GmbH's

- (9): # of bankers holding Bo.rd seats including AG's, GmbH's as well as all other legal incorporations?'

# source: Monopolkommission [32,p.256],Immenga [25,p. 109]

Regardless of how prevalent the presence of bankers in German Boards is, one may not forget the role of the Board as a negotiating forum between capital and labor. Table 4 shows the membership structure of the Boards of the largest 100 firms. As was the intent of the law, not quite half of the Board members are labor representatives. Moreover, the unions associated with the DGB account for more members than all banks combined, and they would certainly have more overlapping goals than the different banks. Not regarding the issue of Board presidency, one may then treat the issue of the power of the DGB with equal importance te the issue of the power of the banks. Moreover, the table confirms the notion voiced above that in the way German law defined Board structure, the banker is not an "add man out".

2CDeviation because of multiple representation 21According to the "Mitbestimmungsgesetz" Jating May 4th 1976, all AG's, GmbH's, as well as KGaA's with more than 2000 employees have to have an Aufsichtsrat with labor representation.

18
_Table 6: Program Effects on Social Rights Deprivations_

||_No controls_||_Controls_||
|---|---|---|---|---|
|**LPM**|**Probit**|**Logit**<br>**LPM**|**Probit**|**Logit**|
|PanelA: Effects on access to|social sec|urity|||
|Treat * year 2014<br>-0.321***|-0.320***|-0.320***<br>-0.306***|-0.346***|-0.365***|
|[-0.026]|[-0.024]|[-0.024]<br>[-0.025]|[-0.028]|[-0.028]|
|Treat<br>0.002|0.002|0.002<br>-0.026|-0.036|-0.033|
|[-0.019]|[-0.020]|[-0.020]<br>[-0.017]|[-0.023]|[-0.024]|
|Year 2014<br>0.010|0.011|0.011<br>0.018|0.017|0.004|
|[-0.018]|[-0.019]|[-0.019]<br>[-0.095]|[-0.119]|[-0.129]|
|Observations<br>13,120|13,120|13,120<br>13,120|13,120|13,120|
|PanelB: Effects on access to|health ca|re services|||
|Treat * year 2014<br>-0.013|-0.014|-0.014<br>-0.020|-0.020|-0.019|
|[-0.021]|[-0.020]|[-0.020]<br>[-0.020]|[-0.019]|[-0.018]|
|Treat<br>-0.003|-0.003|-0.003<br>-0.002|-0.002|-0.001|
|[-0.015]|[-0.015]|[-0.014]<br>[-0.015]|[-0.014]|[-0.014]|
|Year 2014<br>-0.028**|-0.028**|-0.028**<br>-0.038|-0.018|-0.020|
|[-0.014]|[-0.014]|[-0.014]<br>[-0.090]|[-0.074]|[-0.070]|
|Observations<br>13,120|13,120|13,120<br>13,120|13,120|13,120|
|PanelC: Effects on access to|food(food|security)|||
|Treat * year 2014<br>-0.015|-0.015|-0.015<br>-0.011|-0.009|-0.011|
|[-0.021]|[-0.02]|[-0.02]<br>[-0.021]|[-0.021]|[-0.020]|
|Treat<br>0.020|0.020|0.020<br>-0.001|-0.003|-0.001|
|[-0.015]|[-0.016]|[-0.016]<br>[-0.015]|[-0.015]|[-0.015]|
|Year 2014<br>0.010|0.010|0.010<br>0.006|-0.016|-0.016|
|[-0.014]|[-0.014]|[-0.014]<br>[-0.089]|[-0.083]|[-0.082]|
|Observations<br>13,120|13,120|13,120<br>13,120|13,120|13,120|
|PanelD: Effects on quality a|nd spaces o|f the dwelling|||
|Treat * year 2014<br>-0.001|0.000|0.000<br>0.000|-0.003|0.000|
|[-0.014]|[-0.014]|[-0.014]<br>[-0.014]|[-0.011]|[-0.010]|
|Treat<br>0.005|0.005|0.005<br>0.004|0.002|0.001|
|[-0.010]|[-0.010]|[-0.010]<br>[-0.010]|[-0.008]|[-0.007]|
|Year 2014<br>-0.010|-0.010|-0.010<br>0.014|-0.006|-0.001|
|[-0.010]|[-0.010]|[-0.010]<br>[-0.051]|[-0.042]|[-0.039]|
|Observations<br>13,120|13,120|13,120<br>13,120|13,112|13,112|
|PanelE: Effects on access to|basic servi|ces in the dwelling|||
|Treat * year 2014<br>0.005|0.006|0.006<br>-0.002|0.001|0.001|
|<br>[-0.021]|[-0.021]|[-0.021]<br>[-0.016]|[-0.019]|[-0.016]|
|Treat<br>0.016|0.015|0.015<br>-0.008|-0.014|-0.013|
|[-0.016]|[-0.016]|[-0.016]<br>[-0.012]|[-0.013]|[-0.012]|
|Year 2014<br>-0.033*|-0.034*|-0.034*<br>-0.006|-0.041|-0.038|
|[-0.019]|[-0.019]|[-0.019]<br>[-0.059]|[-0.074]|[-0.070]|
|Observations<br>13,120|13,120|13,120<br>13,120|13,120|13,120|

_Continued_ 22
<!-- Start of picture text -->
1.00<br>0.80 0.73 0.70 0.71 0.66 0.71 0.70 0.68<br>0.61 0.60<br>0.60 0.55 0.55<br>0.52<br>0.40<br>0.20<br>0.00<br>Agriculture Manufacturing Other Hospitality Retail Knowledge<br>services intensive<br>services<br>First round Latest round<br>Relative mean deviation<br><!-- End of picture text -->

Note: Relative mean deviation range: [0,1), where values closer to 1 indicate a higher dispersion in the group.

Figure 13: Inequality and levels of online sales by sector

# **5.2 A deeper look into online sales. The case of Vietnam**

To further measure the magnitude of the digital divide between firms, this section compares the online and offline Gini coefficients for sales for the overall sample and the retail sector in Vietnam.<sup>14</sup> The retail sector was among those where online sales were more relevant in general, and in the context of the pandemic. Figure 14 and Figure 15 show the other tale of the digitalization story during the pandemic. The figures show the Lorenz curve that plots the accumulated concentration in online and offline total sales. The results suggest three main facts: First, online markets are dominated by few firms. The top 5th percentile represents more than 80% of the total share of online sales. Second, that inequality is 10 percent higher than what is observed in offline channels. And third, even if more firms entered the digital markets after the pandemic, this inequality has remained stable or even increased during the peak of the pandemic. These facts are observed both for the overall sample and for a restricted sample of retail firms highlighting the importance of having a dominant position in the online markets, and how the shock has made it more relevant.

> 14The choice of Vietnam was motivated by the fact that the data has a good coverage for the 2019 sales.

17
# **Climate Change, Groundwater Salinization and Road Maintenance Costs in Coastal Bangladesh**

Susmita Dasgupta<sup>1</sup> , Md. Moqbul Hossain<sup>2</sup> , Mainul Huq<sup>3</sup> , David Wheeler<sup>4</sup>

**Keywords:** Bangladesh, Coastal Areas, Groundwater Salinity, Infrastructure, Roads

## **JEL Classification: Q54, R49, O18**

* Authors’ names are in alphabetical order

## **This research was funded by the Knowledge for Change Program.**

We would like to extend our special thanks to Dr. Md. Sher Ali for his help with the data and for his expert opinion. We are grateful to Dr. Zahirul Huque Khan for the river salinity data in coastal Bangladesh. <mark>We are also thankful to Dr. Ariel Dinar, Dr. F</mark> orhad Shilpi, Dr. Michael Toman and Dr. Johannes Zutt for their comments and suggestions.

1 Lead Environmental Economist, Development Research Group, World Bank 2 Principal Scientific Officer, Soil Research Development Institute, Ministry of Agriculture, Bangladesh 3 Consultant, World Bank

- 4 Senior Fellow, World Resources Institute
not in the measure, while the ES simply asks ̳how long did it take to get a construction permit?‘ Some of the variation within the ES is no doubt due to the fact that respondents may have different notions of which steps they did and did not include in their assessment. Second, ES respondents are also unlikely to have precise records as to the length of time procedures took, and so will report estimates. Both of these points are potential sources of measurement error, but are unlikely to drive the patterns we see, particularly across countries.

Third, DB has a precisely defined ―standard‖ firm conducting the transaction, e.g. a 10-50 employee, domestically owned private company. The ES are a sample of firms – both larger and smaller than 10-50 employees – and different ownership types. It is possible that some of the variation within country in the ES is that the regulatory requirements themselves vary by firm size or ownership. However, only using those firms that match the characteristics of the ̳standard‘ hypothetical DB firm, leads to the same basic empirical results as the ones we report—but does reduce sample sizes so we stick to the full samples.

The fourth difference between the DB and ES is the most interesting one: the assumption that firms fully comply – and with no direct or third-party facilitation (monetary or otherwise) involved. But firms do not necessarily comply in the ways anticipated in the DB instrument. The respondents in the ES themselves report that additional payments are frequently made to ̳help get things done.‘ Moreover, as we show, if what firms report as their experiences mirrored the formal requirements, the data in the graphs comparing the two across countries would line up along a 45 degree line. The size of the gap between the two sources of data (i.e. how far below the 45 degree line most of the observations in the graphs are) illustrates how costly it would be not to try to strike a deal – and that assuming no deals are made does not, in fact, reflect what firms do.

# **III. Comparing Doing Business and Enterprise Surveys**

What is the connection between the DB indicators and the average value reported by the firms in the ES? For each of the three indicators (imports, operating license, and construction permit) we provide scatter plots (Figures 1a, 1b, 1c) and regression results using a variety of functional forms (Table 1). All three indicators have roughly similar patterns that: 8
# **Table 5(1): Robustness checks, using liberal measure of indirect export share**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Full|Diff|Non-diff|Full|Diff|Non-diff|
|(t-r)|0.606**|1.487***|0.227|1.262***|2.482***|1.197*|
||(0.295)|(0.477)|(0.468)|(0.469)|(0.828)|(0.665)|
|prc_sh|-9.954***|-8.155***|-13.313***|-9.766***|-11.220***|-11.031***|
||(1.402)|(1.858)|(2.411)|(2.388)|(3.904)|(4.165)|
|collpriv_sh|-17.732***|-20.570***|-14.893***|-13.723***|-15.338***|-5.852|
||(2.270)|(3.156)|(3.532)|(3.811)|(5.869)|(6.185)|
|for_sh|-51.697***|-52.720***|-50.537***|-46.729***|-46.093***|-42.923***|
||(1.760)|(2.390)|(2.905)|(2.878)|(4.305)|(5.071)|
|(t-r)*prc||||-0.097|0.857|-0.470|
|||||(0.400)|(0.946)|(0.537)|
|(t-r)*collpriv||||-0.847|-1.418|-1.508*|
|||||(0.604)|(1.266)|(0.792)|
|(t-r)*for||||-1.059**|-1.832*|-1.293**|
|||||(0.474)|(1.021)|(0.652)|
|HS4 fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|7,009|4,176|2,533|7,009|4,176|2,533|
|R-squared|0.550|0.566|0.542|0.551|0.567|0.545|

Notes: The regressions are at HS8 level. The dependent variable is the share of indirect exports (liberal measure), ranging from 0 to 100. The export shares by trade regime and firm ownership type variables range from 0 to 1. The omitted trade regime is normal trade. The omitted firm ownership type is SOE. “Diff” refers to differentiated products according to Rauch’s classification. “Non-diff” products include both homogenous and referenced products. *** p<0.01, ** p<0.05, * p<0.1.

37
old in large-firm-dominated and capital intensive industries whereas that ratio is 2.71, 3.02, and 2.12 in large firm and labor intensive industries, small-firm-dominated and capital intensive industries, and small-firm-dominated and labor intensive industries, respectively.

To explore the role of external financing in the establishment life cycle and firm growth, we divide the sample into industries that are dependent on external finance and those that are not dependent on external finance using the index of external finance dependence (EFD). In col.4 of Table 6, we interact the age dummies with EFD. The age dummy coefficients are all positive and significant, suggesting that on average older firms are larger. The EFD dummy is positive but not significant whereas the interaction of EFD with age dummies is positive and significant. To better interpret the interaction terms, we plot the predictive margins along with the 95% confidence intervals in Figure 14. The figure shows that while firms in industries that are dependent on external finance start out the same size as firms in industries that are not highly dependent on external finance, they grow faster and are larger as they age. Cols. 9 and 10 of Panel B show that the average 40+ year old firms in industries that are not highly dependent on external finance is 2.93 times the size of firms younger than five years old in those industries whereas that number is 4.89 times in industries that are highly dependent on external finance.

Overall, Table 6 and Figures 12-14 show that the initial starting point of firms and their growth rates are different depending on their industries’ technological size, production technology, and dependence on external finance. Firms grow more over their life cycles in industries predicted to be large-firm-dominated based on US data and in capital-intensive industries and in industries more dependent on external finance.

30
effects (i.e., within income groups) more pronounced than the horizontal distributional effects (i.e., across income groups). Alonso and Kilpatrick (2022) also analyze the distributional impact of a carbon tax in 14 economies in Asia and the Pacific region (Australia; China; Hong Kong SAR, China; India; Indonesia; Japan; Kiribati; the Republic of Korea; Mongolia; Myanmar; New Zealand; the Philippines; Singapore; and Taiwan, China). They show that the distributional impacts of a carbon tax vary across the country and the revenue recycling schemes. The costs on households (in terms of consumption loss) of a US$50/tCO2 carbon tax range from less than 2% (Kiribati and Myanmar) to 10% (Mongolia). They find the carbon tax regressive in China, Indonesia, and Mongolia, and progressive in India, Kiribati, Myanmar, and the Philippines.

Studies investigating this issue for Sub-Saharan African economies do not exist. Yet, some countries in the region are showing interest in carbon taxes to help achieve their NDCs under the Paris Agreement or their net zero goals in the long run. The objective of this study is to contribute to filling the research gap and provide new knowledge that could be helpful to policy makers in Ethiopia and similar economies in the region and elsewhere.

Ethiopia also subsidizes fossil fuels. While subsidies are gradually reduced, there was a significant subsidy to petroleum products in 2016, the base year used for this analysis. It would be relevant to understand the economic and distributional impacts of removing fossil fuel subsidies before analyzing the impacts of a carbon tax. It would be also interesting to compare the economic impacts removal of fossil fuel subsidies and the introduction of a carbon tax. It would be further important to analyze the economic impacts when both policies are implemented simultaneously. However, the lack of necessary data in the social accounting matrix (SAM), the main database for the analysis, we could not include the impacts of fossil fuel subsidy removals in this study.

The paper is organized as follows. Section 2 highlights the key features of the CGE model, and the database used for the study, followed by descriptions of scenarios considered in Section 3. The main results and their interpretations are presented in Section 4. This section also presents sensitivity analysis on selected model parameters. Finally, key conclusions and policy implications are derived in Section 5.
been growing at high rates,<sup>15</sup> the vast majority of bank loans has been short term and has financed wholesale and retail trading activities, not investment projects.<sup>16</sup>

**Table 2:  Composition of Total Investment, 2001-04 (** Percentage share of total investment)

||**2001**|**2002**|**2003**|**2004**|
|---|---|---|---|---|
|Construction|29.3|34.9|40.6|41.9|
|Machinery, equipment and tools|44.9|50.6|49.1|46.3|
|o/w mining|14.8|11.2|9.1|15.4|
|Others|25.8|14.5|10.3|11.8|

_Source: Mongolian Statistical Yearbook (2004)._

**Table 3:  Domestic Investment in Mongolia, 2001-2004**

|**Investment**|**2001**|**2002**|**2003**|**2004**|
|---|---|---|---|---|
|Government (% of domestic investment)|38.0|37.2|36.1|35.5|
|Private domestic (% of domestic investment)|62.0|62.8|63.9|64.5|
|Private domestic (% of investment)|21.6|22.7|23.3|26.4|
|Bank loans (% of private domestic investment)|16.5|30.3|30.6|31.9|
|Own funds (% of private domestic investment)|83.5|69.7|69.4|68.1|
|Bank loans(% of investment)|3.6|6.9|7.1|8.4|

_Source: Mongolian Statistical Yearbook (2004)._ Hence, the question that follows from this is whether private investment outside the mining and construction sectors has been low because of high cost of capital or due to low rates of return. This is critical if one needs to ensure that Mongolia’s future growth and employment generation is broad-based.

# **3.2 Is the cost of capital in Mongolia high?**

Although nominal deposit rates remained stable and high in the period 2001-2005, real lending rates came down from 30 percent in 2001 to 11 percent in 2005 (Figure 3).<sup>17</sup> This drop in the real cost of capital was due mostly to a rise in the rate of inflation, rather than a decline in the risk premium (Figure 4). In the absence of any changes to the deposit rate and the risk premium, a drop in inflation from 13 percent in 2005<sup>18</sup> to 4.5 percent in 2006 is likely to reverse the decline in the real lending rate and result in an increase in the real cost of capital above 15 percent. Indeed, real deposit and lending rates have risen significantly as inflation declined in 2007 (confirm what happened to inflation in 2007).

> 15 According to the International Monetary Fund (2005) private credit grew at a rate of more than 200 percent in 2002 and 2003, and 33 percent in 2005. In 2005 domestic credit to the private sector was close to 40 percent of GDP- higher than domestic credit to the private sector in low income countries, but lower than domestic credit in Vietnam, and the average for the middle and high income countries.

> 16 Source: World Bank (2006b) and Fitch Ratings (2006).

> 17 The real lending rate is defined as the rate charged on loans to prime customers on loans of any maturity. 18 In 2005 inflationary pressures in Mongolia were much higher than those in comparator countries such as Vietnam, Cambodia, Kazakhstan, Kyrgyz Rep., and Uruguay.

10
# **Introduction**

One reason why countries care about growth is that growth generates jobs, and jobs are central for living standards and social cohesion (World Bank 2012). Indeed, the recent recession came with large job losses in some European countries, providing stark evidence that jobs and growth are closely entwined (Figure 1). Yet, the relationship is far from straightforward. There are other factors than the state of the business cycle that influence labor market outcomes. Economic regulations and structural reforms affect whether businesses create jobs as the economy expands and destroy jobs as the economy contracts (Orlandi 2012, Bernal-Verdugo, Furceri and Guillaume 2012a and 2012b). In particular, labor market regulations can influence whether employment or wages adjust in response to output changes. For example, the Russian labor market appears to respond to economic shocks through adjustments in wages rather than employment (Gimpelson and Kapeliusshnikov 2011). Likewise, regulations affect whether enterprises can avoid layoffs during recessions with adjustments in the intensive margin of the number of hours, as in some European countries during the global financial crisis (Boysen-Hogrefe and Groll 2010). Furthermore, the sectoral pattern matters (Arias-Vazquez et al 2012). In the 1990s, economic growth in the transition countries of Eastern Europe was driven in large measure by higher labor productivity rather than more employment, as countries moved resources across and within sectors, invested in capital and adopted new technologies (World Bank 2008). In developing economies, employment might be fairly stable over the business cycle due to large selfemployment, on-farm and informal work (Singh, Jain-Chandra and Mohommad 2012). In natural resource rich countries, growth that is driven by higher commodity prices might generate little employment by itself. These factors meant that for a long time the link from growth to employment was tenuous in countries in Europe and Central Asia (ECA), giving rise to the notion of jobless growth (World Bank 2005).

Figure 1: Change in employment and unemployment rates from 2007 to 2010 <!-- Start of picture text --> Peers ECA Peers WE Western Europe ECA<br>Unemployment Rate Employment Ratio<br>4<br>2<br>0<br>Change 2007 to 2010 (%)<br>-2<br><!-- End of picture text --> Note: See Box 1 for country groupings.

2
Table 5: Heterogeneity by individual characteristics —- Effects by age group

|||_Depend_|_ent variable:_||
|---|---|---|---|---|
|||current|employment||
|Age Group:|15 - 24|25 - 34|35 - 44|45 and above|
||(1)|(2)|(3)|(4)|
|Dry shock (SPEI _< −_1_._5)|-0.00332|-0.0319**|-0.0400***|-0.0558***|
||(0.0182)|(0.0146)|(0.0152)|(0.0154)|
|Wet shock (SPEI _>_ 1.5)|0.0643*|0.0374|0.0256|0.0430|
||(0.0341)|(0.0279)|(0.0310)|(0.0310)|
|Controls|Yes|Yes|Yes|Yes|
|Full set of FEs|Yes|Yes|Yes|Yes|
|Observations|227,183|173,653|119,491|60,040|
|R<sup>2</sup>|0.167|0.200|0.221|0.224|

_∗ ∗∗ ∗∗∗_ **_Note:_ p** _<_ **0.1; p** _<_ **0.05; p** _<_ **0.01** . This table reports the coefficient estimates of the impacts of extreme weather conditions (wet and drought shock) on the probability of having a job for subsamples of different age groups. Individuals aged 15-24, 25-34, 35-33 and more than 45 years old are considered. The standard errors are reported in brackets. Standard errors clustered at the grid cell-month-year level are reported in parenthesis.

32