rafmacalaba/datause-encoder-data huggingface dataset:
employees stand out as significant determinants of the Probit host fixed effects.

Columns (5) to (8) of Table 10 report the results obtained for the PPML-N model, which broadly confirm what emerged from the Probit model. The only exceptions are that we find a positive and weakly significant coefficient on the maximum equity share that a foreign company can hold in an acquired domestic company (column (5)), and a negative and weakly significant coefficient on the lack of clarity on the criteria to get a license (column (6)). When we include different types of restrictions in the same specification (column (8)), the policies that still display a significant coefficient are the restriction in setting up a branch, equity restrictions on M&As, lack of transparency on the reason for a license denial, and limits to the nationality of the employees.

Considering the overall results displayed in columns (4) and (8) of Table 10, we conclude that the policies that seem to matter the most in restricting the inflows of M&As in services are restrictions on setting up branches, the rules concerning the nationality of the employees, and the lack of transparency in the denial of licenses. The first indicator may reflect substitution possibilities between modes of entry; the second, the sensitivity of investors to their ability to bring in relevant skills regardless of nationality, which is what makes locations such as Singapore particularly attractive; and the last indicator may reflect the importance investors attach to transparent decision-making by governments.

# **6 Conclusions**

In this paper we present evidence on the determinants of cross-border mergers and acquisitions (M&A) in services sectors. We develop a stylized model of M&A that incorporates firm heterogeneity and inter-sectoral linkages. The model predicts that the incidence of M&A deals depends, inter alia, on the target economy’s size, industrial structure and investment policies, as well as on bilateral transactions costs. We examine these predictions using comprehensive bilateral M&A flow data and detailed information on policy barriers, drawing upon a new database of investment restrictions in services sectors. Detailed gravity estimates show, among other things, that geographical factors affect M&A in services and 26
ECCAS’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE

**Table 4.2. Benchmarking ICT across ECCAS member states**

|Access|Angola|Burundi|Cameroon|Central African<br>Republic|Chad|Congo, Dem.<br>Rep. of|Congo, Rep. of|Equatorial<br>Guinea|Gabon|São Tomé and<br>Príncipe|
|---|---|---|---|---|---|---|---|---|---|---|
|Coverage of mobile network (%<br>of population)|35|60|80|20|50|53|80|30|80|80|
|Mobile telephone subscribers<br>(per 100 inhabitants)|22.3|3.2|24.5|6.8|8.5|8.9|35.4|43.4|87.9|19.4|
|Broadband subscribers (per 100<br>inhabitants)|0.07||0.002|||||0.039|0.25|0.19|
|Internet subscribers (per 100<br>inhabitants)|0.3||0.02|0.06|0.03|||0.25|0.76|1.78|
|International Internet bandwidth<br>(Mbps)|250|4|203|10|6|10|8|17|200|4|
|Prepaid mobile monthly basket<br>(US$)|11.6|11.5|14.4|12.7|16|11.2|18.8|18.6|13.7|9.9|
|Price of a three-minute call to<br>United States (US$)|3.7|2.4|0.6|14.4|12.5|1.2|2.4||2.3|3.8|
|Price of 20-hour Internet basket<br>(US$)|63|52|28|148|105|93|85|35|40|53|
|Price of the fixed-line monthly<br>phone basket (US$)|20.5|2.5|14.3|12.9|8.3||||21.4|10.1|

_Source:_ Ampah and others 2009. _Note:_ Mbps = megabits per second; ICT = information and communication technology.

As far as fixed-line international calls are concerned, ECCAS is the only regional economic community in Africa in which it is cheaper to call outside the community than within it. The average cost of an international fixed-line call within ECCAS, at $1.06 per minute, is higher than calling outside ECCAS, which costs about $0.86 per minute (figure 4.2). The cost of calling outside ECCAS is lower than for any other regional economic community. The cost of an intra-ECCAS call varies from $0.51 in the Democratic Republic of Congo to $2.41 in Equatorial Guinea. In Angola, the Democratic Republic of Congo, Equatorial Guinea, and São Tomé and Príncipe, it is cheaper to call the United States than it is to call within ECCAS. In Cameroon and the Central African Republic, it costs as much to call the United States.

46
Table B2: Effect of Extended Provider Training and Patient Information on Malaria Testing – at the Clinic: Patient Survey No Additional Controls

||(1)<br>Any<br>Malaria<br>Test|(2)<br>RDT Only|(3)<br>Microscopy<br>Only|(4)<br>Multiple<br>Tests|
|---|---|---|---|---|
|Extended Training|-0.0428|0.0525|-0.0359|-0.0667<sup>_∗∗_</sup>|
||(0.0550)|(0.0565)|(0.0688)|(0.0250)|
|Patient Information|-0.0699<sup>_∗∗∗_</sup>|-0.0470<sup>_∗∗_</sup>|-0.0184|0.00677|
||(0.0187)|(0.0202)|(0.0251)|(0.00868)|
|Mean (Basic Training, No Pat. Info.)|0.58|0.21|0.25|0.07|
|N|1973|1973|1973|1973|

_Notes_ : Robust standard errors clustered at the clinic-pair level in parentheses. All regressions include strata and survey date fixed effects. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

Table B3: Effect of Extended Provider Training and Patient Information on the Alloca– tion of Malaria Treatment No Additional Controls

||(1)<br>Full Sample|(2)<br>Has Valid Ho|(3)<br>me-Based RDT|
|---|---|---|---|
||Expected<br>Match|Expected<br>Match|Actual Match|
|Extended Training|0.0506|0.0377|0.0888<sup>_∗_</sup>|
||(0.0329)|(0.0336)|(0.0478)|
|Patient Infomation|0.00134|0.0124|-0.0110|
||(0.0177)|(0.0182)|(0.0341)|
|Mean (Basic Training, No Pat. Info.)|0.44|0.41|0.43|
|N|1971|1093|1093|

_Notes_ : Robust standard errors clustered at the level in parentheses. All regressions include strata and survey date fixed effects. *, **, and *** indicate significance at the 10, 5, and 1 percent levels respectively.

B1
- Narayanan, B. and Walmsley, T.: 2008, Global Trade, Assistance, and Production: The GTAP 7 Data Base, Center for Global Trade Analysis, Purdue University.

- National Research Council: 2005, Valuing Ecosysten Services: Toward Better Eenvironmental Ddecision-Making, National Academies Press.

- Nordhaus, W. D.: 2008, A Question of Balance: Weighing the Options on Global Warming Policies, Yale University Press.

- Paltsev, S., Reilly, J., Jacoby, H., Eckaus, R., McFarland, J., Sarom, M., Asadoorian, M. and Babiker, M.: 2005, The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Version 4, Technical report, MIT Joint Program on the Science and Policy of Global Change.

- Popp, A., Dietrich, J., Lotze-Campen, H., Klein, D., Bauer, N., Krause, M., Beringer, T., Gerten, D. and Edenhofer, O.: 2011, The Economic Potential of Bioenergy for Climate Change Mitigation with Special Attention Given to Implications for the Land System, Environmental Research Letters 6(3).

- Rutherford, T.: 2002, Lecture Notes on Constant Elasticity Functions, http: //www.gamsworld.org/mpsge/debreu/ces.pdf. [Online; accessed 14-April2011].

- Searchinger, T., Heimlich, R., Houghton, R., Dong, F., Elobeid, A., Fabiosa, J., Tokgoz, S., Hayes, D. and Yu, T.: 2008, Use of US Croplands for Biofuels Increases Greenhouse Gases through Emissions from Land-Use Change, Science 319(5867), 1238.

- Sedjo, R.: 1983, The Comparative Economics of Plantation Forestry: A Global Assessment, Johns Hopkins University Press.

- Sohngen, B., Golub, A. and Hertel, T.: 2009a, The Role of Forestry in Carbon Sequestration in General Equilibrium Models, in T. Hertel, S. Rose and R. Tol (eds), Economic Analysis of Land Use in Global Climate Change Policy, Routledge, pp. 279303.

- Sohngen, B. and Mendelsohn, R.: 2007, A Sensitivity Analysis of Forest Carbon Sequestration, in M. Schlesinger (ed.), Human-Induced Climate Change: An Interdisciplinary Assessment, Cambridge University Press, pp. 227237.

39
that the calculations do _not_ represent normative assessments of GHG mitigation policies in the countries named; they are _only_ illustrative numerical examples.

We base our calculations on the Enerdata data set MAC, which was set up to project mitigation implications of carbon taxes introduced at particular future dates.<sup>10</sup> Our calculations are based on the “Enerbase” simulations created by Enerdata, where it is assumed that no climate policy is applied up to 2025 in these countries under business as usual (and where LULUCF emissions are ignored).<sup>11</sup> In our application of Enerdata, we consider two different carbon taxes in 2025, namely $10 and $20 per ton of CO2 e. We phase in the carbon tax linearly over time: in the $10 case, for example, the tax is zero in 2020, $2 in 2021, $4 in 2022, etc.

The Enerdata framework is somewhat broader than the simple static framework considered in Sections 2-4. The most important difference is that with the Enerdata approach, the quantity of mitigation resulting from doubling the carbon tax is less than double that from the initially chosen carbon tax. This reflects a presumption that a significant amount of “low-hanging fruit” can be harvested as mitigation outcomes at relatively low carbon taxes. In the model framework laid out in Sections 2-4, by contrast, mitigation outcomes are proportional to the implemented carbon tax. Similarly, in the conceptual model in Sections 2-4, support payments tend to increase with the square of the carbon tax rate, since a higher carbon tax leads to a proportionally higher mitigation rate. Since mitigation in the Enerdata simulations increases by less than the carbon tax, support payments grow more slowly than the square of the tax rate.

Table 2 gives figures for mitigation effected in the five sample countries, due to imposing a carbon tax which increases linearly by year, from zero today up to either $10 or $20 per ton CO2e by 2025. Column 5 gives mitigation in 2025, relative to the BAU emissions rate in the same year, due to imposing these two alternative tax levels by 2025. The rightmost column gives the cumulative

> 10 For reference to Enerdata and the data sets referenced, see the following website: https://www.enerdata.net/.

> 11 The other relevant simulation alternative from Enerdata, called “Enerblue”, assumes that certain climate policies have already been implemented by the host countries which reduces the mitigation scope from using carbon taxation, and this reduces the needed support. This alternative is not invoked in our calculations, but might have been the better alternative if the host countries in question had already started to implement (or were expected to implement) their NDCs before support would be received. They would also give lower overall payouts to hosts as the induced mitigation amounts are smaller.

17
2 hold when allowance is made for the (seemingly) higher cost-of-living in the modern urban sector. Empirical research on poverty has sometimes addressed this issue; for example, widely used poverty lines for Indonesia are set at substantially higher levels for households living in urban areas than in rural areas.<sup>5</sup> And the poverty line differentials in past spatial welfare comparisons have often been crucial to sectoral rankings. For example, the 66 percent differential in poverty lines between urban and rural sectors assumed by the Indonesian Central Bureau of Statistics (BPS, 1984) for 1981 is more than sufficient to reverse the poverty ranking of sectors in terms of the headcount index (over that obtained at a zero differential). Similarly, the urban-rural differential in poverty lines assumed in the influential study by Dandekar and Rath (1971) of poverty in India in the 1960s was also sufficient to reverse the sectoral ranking of headcount indices. Clearly the assumptions made about sectoral cost-ofliving differentials in past work may have had considerable bearing on the empirical results and their policy implications.6 The issue merits further investigation.

However there are a number of problems in using observed housing rents and food prices to make spatial cost-of-living comparisons. Two stand out: i) 'Housing" is a highly heterogeneous good and so observed rents can be a poor price index; for example, the considerably higher expenditures on housing in urban areas relative to rural areas typical of developing countries undoubtedly reflect higher consumption levels of certain housing attributes as well as (possible) higher prices for those attributes. Observed housing expenditures thus reflect, at least in part, income differences.
21 <!-- Start of picture text --> Figure 11: Lorenz Curve of Flight Capital weighted by GDP<br>World Bank Residual, Stocks - 1991<br>0,8 -<br>0 a<br>Shtia of Stock of<br>Total Flight Capital<br>0.4<br>0 0.1 0.2 0.3 0.4 05 0F . 0.7 0.8 0.9 1<br>GDP Deciles<br><!-- End of picture text --> When deciles of GDP are ranked against deciles of share of capital flight stock, the picture changes drastically (Figure I1). The curve is close to (but above) the 45 degree-line, indicating that countries with smaller GDP have a higher share of capital flight. Capital flight is thus not just widespread but is a substantial drain of scarce external resources for many developing countries.
large extent due to tax regulations that stipulate that the so-called offshore firms do not have to pay import duties. As alluded to above, this minimizes their incentives to engage in tariff evasion, rendering such firms a useful comparison group against which to benchmark the behavior of other firms. Fourth, Tunisian customs authorities are considered among the most corrupted of all government institutions by Tunisian citizens and companies (ATCP, 2015, ITCEQ, 2012). While the Tunisian customs code is consistent with best practices defined by the World Customs Organization,<sup>2</sup> its implementation is discretionary (World Bank 2014).<sup>3</sup> The combination of a bewildering complexity of import regimes allowing for suspension of import duties and very weak administrative controls, including a severely defunct IT system, render effective enforcement challenging. Last but not least, Tunisia has great administrative data and authorities willing to share those, enabling us to unambiguously identify politically connected firms as those who were confiscated in the aftermath of the Jasmin Revolution.

By providing evidence suggesting that politically connected firms are more likely to evade tariffs, this paper contributes to and combines different strands of literature. To start, a large literature, pioneered by Bhagwati (1964, 1967) and popularized by Fisman and Wei (2004), has used 2 A new customs code compliant with “best practices” was adopted in 2009 just months before the Jasmin revolution. 3 Like many other countries, Tunisian’ customs maintain a three-track system whereby customs declarations allocated to the ‘green corridor’ are allowed to pass through customs without any inspection, customs declarations allocated to the ‘yellow’ corridor face document inspections and customs declarations in the ‘red’ corridor are subject to physical inspections. Customs officers, however, were reluctant to disclose both which firms were allocated to these various corridors and the criteria used to make such allocations. In all cases, containers have to go through a scanner due to a 100% scanning policy, which means that there is a minimal control for any container. Document checks are carried out systematically. Therefore, any firm is subject to controls and therefore subject to discretion from customs officers.

4
Table 7: Descriptive Statistics - Forecast survey

||Mean|p50|SD|Min|p25|p75|Max|N|
|---|---|---|---|---|---|---|---|---|
|_Filing probability (p.p.)_|||||||||
|Pooled|3.9|4.0|3.4|-6.0|2.0|5.0|20.0|64|
|Sanctions|6.7|5.5|4.6|1.0|4.0|8.0|22.0|62|
|Procedures|5.3|4.0|5.5|0.0|2.0|7.0|23.0|63|
|Tax morale|1.6|1.0|3.8|-5.0|0.0|3.0|20.0|61|
|_Gross revenue (%)_|||||||||
|Pooled|6.7|5.0|6.2|-3.0|2.5|10.0|25.0|64|
|Sanctions|11.5|9.5|10.8|-4.0|5.0|13.0|50.0|64|
|Procedures|9.3|5.0|12.6|-2.0|1.0|11.0|58.0|63|
|Tax morale|2.3|0.5|6.1|-18.0|0.0|3.0|36.0|62|
|Low risk|6.3|1.0|9.4|-5.0|0.0|8.0|38.0|61|
|Medium-low risk|6.1|4.0|8.0|-7.0|0.0|8.0|28.0|61|
|Medium risk|7.0|4.0|8.8|-3.0|0.0|11.0|50.0|61|
|Medium-high|8.8|7.0|10.6|-3.0|1.0|12.0|50.0|61|
|High risk|9.1|6.0|11.5|-5.0|0.0|15.0|51.0|61|
|_Taxable income (%)_|||||||||
|Pooled|5.7|4.0|5.7|-2.0|2.0|10.0|30.0|64|
|Sanctions|8.6|6.0|9.2|-4.0|3.0|12.0|50.0|63|
|Procedures|6.5|4.0|10.0|-2.0|1.0|8.0|57.0|63|
|Tax morale|1.4|0.0|4.7|-12.0|0.0|2.0|21.0|63|

_Note_ : This table presents descriptive statistics for the expert forecast survey. The first panel refers to forecasts of treatment effects on filing probability, the second panel on declared gross revenue and the third panel on declared taxable income. See Appendix E for details on sample restrictions.

30
**Table 3: Estimates of the impact of the change in market access on cropland area (roads and pop.)**

||(1)<br>OLS|(2)<br>OLS|(3)<br>FE|(4)<br>FE|
|---|---|---|---|---|
|ΔroadLn MA_t_|0.026***|0.016***|0.032***|0.007***|
||(0.00)|(0.00)|(0.00)|(0.00)|
|Δpop.Ln MA_t_|0.001|0.457***|0.042***|0.418***|
||(0.00)|(0.00)|(0.01)|(0.01)|
|Shrinking cropland_t-_1||-0.330***<br>(0.00)||-0.212***<br>(0.00)|
|ΔroadLn MA_t_× shrinking cropland_t-_1||0.015***<br>(0.01)||0.021***<br>(0.01)|
|Δpop.Ln MA_t_× shrinking cropland_t-_1||-1.218***<br>(0.02)||-1.158***<br>(0.01)|
|Ln cropland_t-_1||-0.060***<br>(0.00)||-0.312***<br>(0.00)|
|Avg. rainfall_t_||0.113***<br>(0.00)||-0.203***<br>(0.01)|
|(Avg. rainfall_t_)<sup>2</sup>||-0.010***<br>(0.00)||0.016***<br>(0.00)|
|Ln pop. density_t-_1||-0.129***||-0.280***|
|||(0.00)||(0.01)|
|Ln dist. to major port||-0.002*<br>(0.00)|||
|Constant|0.012***|-0.160***|0.010***|0.806***|
||(0.00)|(0.01)|(0.00)|(0.01)|
|Country×year dummies|No|Yes|||
|Observations|1,161,664|1,160,235|1,161,664|1,160,235|
|Number of groups|||290,416|290,130|
|R-squared|0.000|0.190|0.000|0.165|

Notes: This table presents estimates from OLS (columns 1 and 2) and fixed effect (columns 3 and 4) regressions of the change in the natural logarithm of cropland area between year t and t – 1 on the change in the natural logarithm of market access due to roads between year t and t – 1 ( _Δroad Ln MA t_ ) and due to population between year t and t – 1 ( _Δpop. Ln MA t_ ). Controls included in the OLS regression (column 2): dummy variable indicating a decrease in cropland during the previous period (Shrinking cropland t-1), interaction term between the change in the natural logarithm of market access due to roads and the dummy variable ( _Δroad Ln MA t_  _Shrinking cropland t-_ 1), interaction term between the change in the natural logarithm of market access due to population and the dummy variable ( _Δpop. Ln MA t_  _Shrinking cropland t-_ 1), natural logarithm of lagged cropland area ( _Ln MA t-_ 1), natural logarithm of average precipitation over the period ( _Ln avg. rainfall t_ ), natural logarithm of lagged population density ( _Ln pop. density t-_ 1), natural logarithm of distance to nearest major port ( _Ln dist. to major port_ ), country dummies, year dummies, and country × year dummies. Controls included in the FE regressions (column 4): dummy variable indicating a decrease in cropland during the previous period, interaction term between the change in the natural logarithm of market access due to roads and the dummy variable, interaction term between the change in the natural logarithm of market access due to population and the dummy variable, natural logarithm of lagged cropland area, natural logarithm of average precipitation over the period, and natural logarithm of lagged population density. Robust standard errors are in parenthesis, where *** is significance at the 1% level, ** significance at the 5% level, and * significance at the 10% level.

15
Policy Research Working Paper 8188

# **Abstract**

Combining nightlight data as a proxy for economic activity with remote sensing data typically used for natural hazard modeling, this paper constructs novel damage indices at the district level for Indonesia, for different disaster events such as floods, earthquakes, volcanic eruptions and the 2004 Christmas Tsunami. Ex ante, prior to the incidence of a disaster, district-level damage indices could be used to determine the size of the annual fiscal transfers from the central government to the subnational governments. Ex post, or after the incidence of a natural disaster, damage indices are useful for quickly assessing and estimating the damages caused and are especially useful for central and local governments, emergency services, and aid workers so that they can respond efficiently and deploy resources where they are most needed.

This paper is a product of the Poverty and Equity Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at eskoufias@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