rafmacalaba/datause-encoder-data huggingface dataset:
Figure A8: Specification Charts for Deviations in Days with Rain <!-- Start of picture text --> Ethiopia Malawi<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.02 .04<br>.01 .02<br>0 0<br>* Weather Product * -.01 * Weather Product * -.02<br>TAMSAT -.02 TAMSAT -.04<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Niger Nigeria<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.04 .03<br>.02 .02<br>0 .01<br>-.02 0<br>-.04 -.01<br>* Weather Product * -.06 * Weather Product * -.02<br>TAMSAT TAMSAT<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br>Tanzania Uganda<br>p < 0.05 n.s. p < 0.05 p < 0.05 n.s. p < 0.05<br>.015 .04<br>.01 .02<br>.005<br>0 0<br>-.005 -.02<br>* Weather Product * -.01 * Weather Product *<br>TAMSAT TAMSAT -.04<br>ERA5 ERA5<br>ARC2 ARC2<br>MERRA-2 MERRA-2<br>CPC CPC<br>CHIRPS CHIRPS<br>* Dep. Var. * * Dep. Var. *<br>Value Value<br>Quantity Quantity<br>* Model * * Model *<br>Weather + FE + Inputs Weather + FE + Inputs<br>Weather + FE Weather + FE<br>Weather Weather<br>0 4 8 12 16 20 24 28 32 36 0 4 8 12 16 20 24 28 32 36<br><!-- End of picture text --> _Note_ : The figure presents specification curves, where each panel represents a different country, with three different models presented within each panel. Each panel includes 36 regressions, where each column represents a single regression. Significant and non-significant coefficients are designated at the top of the figure. For each Earth observation product, we also designate the significance and sign of the coefficient with color: red represents coefficients which are negative and significant; white represents insignificant coefficients, regardless of sign; and blue represents coefficients which are positive and significant.

56
b Shimantan/Banqiao Dam Failure - Thayer Watkins. "The Catastrophic Dam Failures in China in August 1975". San Jose State University. Retrieved on July 7 2017.

c Machchu dam: Noorani, A. G. (25 August 1979). "The Inundation of Morvi". Economic and Political Weekly. 14 (34): 1454 and Noorani, A. G. (21 April 1984). "Dissolving Commissions of Inquiry". Economic and Political Weekly. 19 (16): 667–668.

> d Chernobyl: The Chernobyl accident - UNSCEAR's assessments of the radiation effects, <u>http://www.unscear.org/unscear/en/chernobyl.html</u>

e Ilya Naymushin (2009-08-17). "Russian dam disaster kills 10, scores missing". Reuters. http://www.reuters.com/article/us-russia-accident-sbidUSTRE57G0M120090817?sp=true f Normile, D. (27 July 2012). "Is Nuclear Power Good for You?". Science. 337 (6093): 395–396. doi:10.1126/science.337.6093.395-b; Hoeve, J.E.T. and M. Z. Jacobson (2012). "Worldwide health effects of the Fukushima Daiichi nuclear accident". Energy & Environmental Science. 5 (9): 8743.

40
about 6 percent when reference prices were very low in 1985. The shares of the individual subsidies in reference prices are illustrated in Fig 24 .

The details of the ESC estimates are given in Annex 23 and the results are summarised in Table 14. As noted previously, as are the EPC estimates, they have been calculated for the importable hypothesis only. The exportable hypothesis ESCs could easily be estimated from the spread sheet data. Comparing incentives between the three states, the ESCs make little difference to the result suggested by the EPCs i.e that during this period UP farmers were considerably more protected (average ESC = 2.65) than farmers in Maharashtra (average ESC = 1.20 ) and TN (average ESC = 1.48).

Tablel4: **Effective subsidy coefficients for cane, 1981-90**

||1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|Avg.|
|---|---|---|---|---|---|---|---|---|---|---|---|
|UP|0.56|1.05|1.11|1.98|8.13|6.18|3.98|1.80|0.94|0.80|2.65|
|Maharashtra|0.46|0.79|0.95|1.39|2.29|1.81|1.74|1.35|0.73|0.53|1.20|
|TN|0.51|0.90|1.33|1.86|3.84|2.57|1.60|0.97|0.53|0.65|1.48|
|All India|0.60|1.12|1.29|2.21|6.61|4.89|3.33|1.88|0.97|0.86|2.38|

Sources and notes: See Annex 22.

The NPCs,,EPCs and ESCs are compared in Table 15 and Fig 25. In 7 of the 10 years, by all the indicators sugar cane growing was heavily protected. In each year the EPCs was higher than the NPC, indicating that after taking account of the protection of tradable inputs, protection of value added in cane growing was higher than nominal protection of the cane. Likewise, in each year the ESCs are higher than the EPCs , reflecting the increase in the cane growing processing margin resulting from the subsidies on the non-tradable inputs. But the general level of incentives was dominated by the large swings in world prices of sugar as reflected in the nominal protection coefficients. The proportionate effect of the protection of tradable inputs and the non-tradable subsidies on actual relative to reference processing margins, was quite low in the early and later years of the 1980s, but much greater in the mid 1980s when world sugar prices and hence the reference processing margins were very low.

Table 15 **:NPCs, EPCs and ESCs for sugarcane, 1981-90**

||1981|1982|1983|1984|1985|1986|1987|1988|1989|1990|Avg.|
|---|---|---|---|---|---|---|---|---|---|---|---|
|NPC|0.55|0.98|1.11|1.77|3.81|3.10|2.52|1.52|0.83|0.74|1.69|
|EPC|0.54|0.99|1.12|1.89|5.66|4.21|2.85|1.60|0.84|0.75|2.04|
|ESC|0.60|1.12|1.29|2.21|6.61|4.89|3.33|1.88|0.97|0.86|2.38|

Sources and notes: See Annexes 18 and 22.

52
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE leading operators has reportedly been established for developing the connection to undersea cables, which presumably will enhance open access ( _TelecomPaper_ , June 5, 2009).

Internet penetration in Angola is average for the southern African region (figure 20). There are no official data on the number of Internet users in Angola. But according to the government, there were some 300,000 subscribers in 2008; assuming 3 users to a subscription, this amounts to about 900,000 users or 5.4 percent of the total population.<sup>7</sup>

**Figure 20. Angola’s Internet market benchmarked against southern African peers**

<!-- Start of picture text --> a. Internet service trends, Angola, 2000–08 b. Internet service trends, southern Africa, 2008<br>20 350<br>6.0 25<br>18<br>300<br>5.0 16<br>20<br>14 250<br>4.0<br>12<br>200<br>15<br>10<br>3.0 150<br>8<br>10<br>6 100<br>2.0<br>4<br>50<br>5<br>1.0 2<br>0 0<br>0.0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br>Internet users (per 100 people)<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per<br>International Internet bandwidth (bits per second per person)<br>person)<br>Internet users<br>Internet users<br>International Internet bandwidth<br>International Internet bandwidth<br>Madagascar Malawi Zambia Lesotho Mozambique Zimbabwe Botswana Angola South Africa Namibia Mauritius<br><!-- End of picture text --> _Source:_ Minges 2008.

Several of the new fixed-line operators have launched WiMAX<sup>8</sup> networks, which, along with Angola Telecom’s ADSL<sup>9</sup> service, provides a degree of fixed broadband competition. Movicel recently launched a GSM network that should enhance intermodal competition in the mobile market.<sup>10</sup> It is likely that it will also launch mobile broadband based on HSDPA<sup>11</sup> technology to complement its high-speed wireless EVDO<sup>12</sup> network. Unitel has also launched mobile broadband services. Given these developments and the availability of international access through up to three undersea fiber-optic cables in the near future, the prognosis for Angola’s broadband infrastructure is positive. The government will need to enhance

> 7 Conselho de Ministros,  ―Programa Executivo do Sector para 2009,‖ Resolução n.o 33/09 de 7 de Maio.

> 8 Worldwide interoperability for microwave access.

> 9 Asymmetric digital subscriber line.

> 10 www.consuladogeral-angola.hk/sub/Press/Press_2010_1201_3.html.

> 11 High-speed downlink packet access.

> 12 1x Evolution-Data Optimized.

34
evaluated at ...ve year experience is 1.8 percent a year and declines to 1.4 percent when evaluated at ten years of experience. It is a well documented fact that returns to home country experience of immigrants are on average low compared to returns to experience of native born US citizens. Using data from the 1970 Census, Chiswick (1978), for example, reports returns to experience of 1.4 percent for immigrants and 2.1 percent for US natives. Adding openness to the regression and estimating equation (6) leads to the estimates presented in Table 3. The estimated return to home country experience amounts to 2.1 percent evaluated at 5 years of labor market experience and the mean of log Open. Imposing the constraints on b0 and �2 implied by equation (6) reduces the estimated return to home country experience to 1.6 percent. The coe¢ cient on the interaction term between log Open and experience has a positive sign and is signi...cant.<sup>10</sup> Increasing Open from 0.2 to 0.3 raises the return to home country experience by 0.2 percentage points if coe¢ cients are unconstrained.<sup>11</sup> Taking a country from the 10th percentile to the 90th percentile of log Open raises the estimated return to home country experience by 1.2 percentage points and increases to 1.3 percentage points in the constrained regression.

Based on the results, Figure 1 displays the predicted log earnings-experience pro...les for two US immigrants. Both immigrants are assumed to have the same individual characteristics, but the source countries of the immigrants di¤er in their degree of openness. As equation (6) imposes that neither the intercept of the Mincerian earnings equation nor the return to individuals characteristics vary with country of origin, the intercept of the log earningsexperience pro...le is the same for both immigrants. Moreover, since equation (6) assumes that openness a¤ects only the slope but not the curvature of the pro...le, the di¤erence in returns to experience among the two immigrants remains constant throughout their work life.

Restricting the intercept of the Mincerian earnings equation to be constant across countries may lead to a biased estimate of the e¤ect of openness on the slope of the log earnings-experience pro...le.<sup>12</sup> If initial wages of US immigrants from more open economies are higher, then the e¤ect of open-

> 10 The calculation of the standard errors takes into account heteroscedasticity and clustering. Not controlling for clustering may lead to a serious downward bias in the OLS standard errors when adding aggregate market variable to micro units. (see Moulton (1986, 1990))

> 11 The change in Open from 0.2 to 0.3 corresponds approximately to an increase from the 20th percentile to the median value. To give an example, this change corresponds to South Korea (Open equals 0.20) as compared to Taiwan (0.32) or Australia (0.20) as compared to Canada (0.33).

> 12 It may also lead to a biased curvature of the pro...le.

9
also determine the disposition of countries to reform their institutional structures as well as the pace of reforms. We do not use the measures of settler mortality as proposed by Acemoglu et al (2001) as this cuts our sample by more than a half.

Third, faster growth may also bring about improvements in the institutional environment. For instance, as countries grow faster, the level of education may improve and there may be a greater receptiveness for institutional improvements and reforms. In addition, the rule of law index is constructed from opinion surveys and other subjective measures. The responses of the experts engaged in the surveys may be biased by the current state of the economy. Also there are likely to be omitted variables correlated with both per capita real incomes and institutional quality in our regression such as political regime characteristics. We instrument here the rule of law index with the same variables as the regulation index.

Table 3 reports the results for openness. The first column repeats the corresponding OLS results (column 2, Table 1) for reference. First, we instrument only for the interactive term using predicted trade, the language variables, and legal origin as instruments. The coefficient on the interactive term increases (as does the coefficient on openness and the dummy) and remains negative and highly significant.

Column 3 reports the results using the same set of instruments, but instrumenting for openness, the regulation dummy, and the regulation index, as well as the interactive term. With the exception of openness, which is no longer significant, the sign and significance of all of the endogenous variables remains unchanged.

Column 4 reports the results using two stage instrumental variables. In the first stage, we regress the dummy and the interactive term on a selection of the excluded exogenous variables 16
administrative costs are probably higher than in other cash grants programs that are discussed in the literature.

Both treatments should also have improved entrepreneurs’ business practices, especially given the close monitoring and advisory role MEBF takes. Moreover, having won in the lottery may also have increased entrepreneurs’ confidence and improved their attitudes toward their business (McKenzie 2017). It may also send positive signals to financial institutions and through this channel relax their credit constraint.

Both matching and cash grants to firms usually also raise concerns regarding their additionality, that is, whether firms receiving grants would not have undertaken the subsidized activities in any case, even without the subsidy. This would be expected to happen if firms were not credit constrained (McKenzie 2017). Yet, we believe this is not true for most entrepreneurs and inconsistent with what firms report in terms of capital needs and access to credit.

# **4. Randomization, balance tests, and attrition**

# _4.1 Randomization_

Earlier, we explained how MEBF attracted firms and prospective firms into the business plan competition and selected the most promising ones for participation in the public lottery. In this subsection, we provide more details about the stratified randomization.

In each municipality, we stratified applicants according to the size of the financial support they requested and justified in their business plan. Given that the amount requested in cash could differ from the amount requested for a matching grant, we used the requested amount in cash for all firms for that purpose. This amount was in almost all cases higher than the one requested for a matching grant. Within each stratum, each applicant had the same chance to be allocated to one of the three groups (matching, cash, and control). Table 1 shows the number of entrepreneurs assigned into each stratum and randomly assigned to one of the three groups.

**Table 1. Number of entrepreneurs per stratum and treatment arm**

|Blocks|Cash amount<br>|Cash group<br>|Matching group<br>|Control group<br>|Total<br>|
|---|---|---|---|---|---|
||(US$)|(#)|(#)|(#)|(#)|
|1|≤ 1,999|68|68|70|206|
|2|2,000–2,999|153|154|153|460|
|3|3,000–3,999|106|106|105|317|
|4|4,000–4,999|42|41|41|124|
|5|5,000–5,999|12|12|12|36|
|6|6,000–6,999|5|5|7|17|
|7|7,000–8,000|14|14|12|40|
||Total(#)|400|400|400|1,200|

_Source:_ Data from MEBF’s report on the public lottery (2019).

Table 2 shows the mean and distribution within each treatment group. On average, every entrepreneur was granted US$3,420, which is substantially lower than the maximum grant that could be granted. Few firms requested US$5,000 or more. This is interesting information regarding the sustainability of such a 10
the original variables used to predict fragility as controls without changing results. It is only in the post-war period that exclusion and refugees become a factor that influences foreign investment flows. Finally, the results we find are robust across all datasets of foreign investment we use.

These results make it at least plausible that political exclusion and refugees matter because they predict a relapse to more intense violence. As final piece of evidence for this idea we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD). We collected data on political risk evaluations from ONDD who, according to their annual report, insured transactions worth about 7 billion EUR in 2011. The variable we use measures the risk of a credit default for reasons beyond the control of the debtor, i.e. due to political or financial macroeconomic events. We choose this variable because it provides the most consistent time-series in the ONDD data. ONDD measures both short- and mid-term risk on a scale from 1 (low risk) to 7 (high risk). Table 12, columns (1) and (4) show that risk ratings are decreasing in peacetime. Note that, as before, we control for country fixed effects which implies that we look at changes within country. Within-country risk falls significantly in peacetime. The effect is also economically meaningful - about one quarter of a standard deviation in the case of short term risks. In columns (2) and (5) we show the specification in which we add a dummy for the first five years of recovery and fragile peace. The coefficient on fragile peace is positive and of similar size in both cases. Mid-term risk is evaluated significantly higher in periods that are followed by conflict. In columns (3) and (6) we include the fitted values gained from a regression of fragility on refugees and political exclusion. Again the fitted values predict higher risk evaluations by ONDD. The estimate is not very precise but quantitatively large both for short- and mid-term evaluations. A rise in the fitted risk by 10 percentage points coincides with an increase in risk evaluations by 0.08 to 0.16. Evaluations like this have real-life repercussions as they are used to decide on insurance premiums.

Our results signal a clear margin for policy. Attracting foreign investment appears to be a lot harder if a government excludes or even discriminates against parts of the population and refugees have not returned to their homes. We argue that this is true even if investors only care about stability. In this view, investment can be attracted to a country through policies that de-escalate conflict and commit the warring parties to peace in the period right after violence stops.

56
Italy, Netherlands, Poland and Spain. Table 1 lists the top 15 countries in recorded emissions in CITL. The ten countries covered in the analysis are among the biggest polluters. Table 2 shows the proportion of CITL installations that are matched – the ratio of matched firms and total firms in each CITL sector. Though the power sector has a low match ratio, most of the non-matched firms are identified as being in non-power sectors, therefore we believe that our control group will not contain ‘treated’ firms.

Table 1: Top 15 Polluting Countries

|Rank|Country|Verified Emissions<br>(million tonnes)|
|---|---|---|
|1|Germany*|466|
|2|Great Britain*|247|
|3|Italy*|213|
|4|Poland*|203|
|5|Spain*|162|
|6|France*|122|
|7|Netherlands*|81|
|8|Czech Republic*|80.6|
|9|Greece|67.9|
|10|Belgium*|52.5|
|11|Finland|38.7|
|12|Romania|38.3|
|13|Austria*|31.3|
|14|Portugal|30.5|
|15|Denmark|27.9|

Note: The ‘verified emissions’ is a simple time average of the total verified emissions of all installations on the CITL record. Starred countries indicate the ones in our sample. The names and addresses of Greek firms in the AMADEUS database did not transcript properly so we did not try to match these installations and their respective firms. * indicates countries that are covered in the study.

8
Singaporean subjects were just as likely to accept bribes, and were significantly more likely to accept than Australian and Indonesian subjects. This is a puzzling result: Despite their lowcorruption CPI ranking, Singaporeans were much more tolerant of corruption, while high-CPI Indonesians were much less tolerant. Their results are able to rule out ethnicity as a driver of corrupt behavior, and underline the importance of values transmitted through institutions. The authors argue that this tolerance of corrupt behavior in Singapore (and the intolerance of corrupt behavior in Indonesia) is due to recent institutional changes implemented in these countries.

The authors also examine the relationship between the prevalence of bribery and its impact on welfare by systematically varying the welfare effect of bribery. They find no significant treatment effects in Indonesia, or Singapore, but find that Australians are significantly less likely to punish when bribes are welfare-reducing. Furthermore, subjects were also more likely to offer and accept more bribes in anticipation of the reduced propensity to punish. While the results are not as clear as one might wish, they are illuminating of two major concerns in corruption research. First, they cast doubt on the generalizability of lab results to other cultures and settings. That is, culture and context are important factors to consider when using the lab as venues to test anti-corruption policies. Secondly, (assuming the results are generalizable) if the results found by Cameron et al. (2009) are indeed reflective of recent institutional changes, then lab based corruption measures can be used to gauge the impact of such changes on corrupt behavior in societies. That is, while perceptions-based measures may be sticky and therefore unresponsive to large shifts in public policy, behavior-based measures may respond more quickly. For example, the rankings for each of the countries in the sample have remained relatively unchanged in the CPI since 2003 (Australia: 8<sup>th</sup> in 2003 and 2010; Singapore: 5<sup>th</sup> in 2003, 1<sup>st</sup> in 2010; India: 83<sup>rd</sup> in 2003, 87<sup>th</sup> in 2010; Indonesia: 122<sup>nd</sup> in 2003, 110<sup>th</sup> in 2010). However, what is more likely is that the lab measures are capturing one particular type of corruption, whereas the perceptions measures capture perceptions more generally. For example, the Bribe Payers Index by transparency international focuses on petty corruption and bribery in particular. For both 2006 and 2008, Australia ranked above Singapore; with India close to the bottom of the rankings (Indonesia was not measured).

Barr and Serra (2010) conduct a lab study to test the robustness of the results found by Fisman and Miguel (2007) in a lab setting. Using undergraduate students at Oxford University, the authors correlate subject behavior in a simple bribery game with their country of origin.

12